| Fact-Checking Complex Claims with Program-Guided Reasoning |
| LiangmingPan1,2 XiaobaoWu3 XinyuanLu4 AnhTuanLuu3 |
| WilliamYangWang1 Min-YenKan4 PreslavNakov2 |
| 1 UniversityofCalifornia,SantaBarbara 2 MBZUAI |
| 3 NanyangTechnologicalUniversity 4 NationalUniversityofSingapore |
| liangmingpan@ucsb.edu xiaobao002@e.ntu.edu.sg luxinyuan@u.nus.edu |
| anhtuan.luu@ntu.edu.sg william@cs.ucsb.edu |
| kanmy@comp.nus.edu.sg preslav.nakov@mbzuai.ac.ae |
| Abstract Instead,ahumanfact-checkerneedstodecom- |
| posetheclaim,gathermultiplepiecesofevidence, |
| Fact-checking real-world claims often re- |
| andperformstep-by-stepreasoning(Nakovetal., |
| quires collecting multiple pieces of evidence |
| 2021a),asillustratedinFigure1. Thismakesveri- |
| and applying complex multi-step reasoning. |
| fyingcomplexclaimsmuchmorechallengingthan |
| In this paper, we present Program-Guided |
| Fact-Checking (PROGRAMFC), a novel fact- thetypicalsettingexploredinpreviouswork,where |
| checking model that decomposes complex informationfromasinglearticleissufficienttosup- |
| claimsintosimplersub-tasksthatcanbesolved port/refutetheclaim(Thorneetal.,2018;Saakyan |
| usingasharedlibraryofspecializedfunctions. |
| etal.,2021;Schusteretal.,2021;Panetal.,2021; |
| Wefirstleveragethein-contextlearningability |
| Waddenetal.,2022a;Krishnaetal.,2022). |
| of large language models to generate reason- |
| ingprogramstoguidetheverificationprocess. Besides multi-step reasoning, we still need to |
| Afterward,weexecutetheprogrambydelegat- considertwokeyaspectsfordevelopingareliable |
| ingeachsub-tasktothecorrespondingsub-task fact-checkingsystem: (i)Explanability: Themodel |
| handler. This process makes our model both should not only predict the veracity of the claim, |
| explanatoryanddata-efficient,providingclear |
| butitshouldalsoprovideaclearexplanationofits |
| explanationsofitsreasoningprocessandrequir- |
| reasoningprocesstohelpusersunderstandandtrust |
| ingminimaltrainingdata. Weevaluate PRO- |
| theresults. (ii)Dataefficiency: Humanannotation |
| GRAMFC on two challenging fact-checking |
| is often time-consuming, costly, and potentially |
| datasets and show that it outperforms seven |
| fact-checkingbaselinesacrossdifferentsettings biased,makingitdifficulttocollectsufficienthigh- |
| of evidence availability, with explicit output qualitylabeleddataformodeltraining,particularly |
| programsthatbenefithumandebugging.1 for complex claims. Therefore, it is desirable to |
| buildamodelthatcanperformwellwithminimal |
| 1 Introduction |
| or no training data. Despite a few models (Zhou |
| Theproliferationofdisinformation,e.g.,insocial etal.,2019;Zhongetal.,2020;AlyandVlachos, |
| media,hasmadeautomatedfact-checkingacrucial 2022) being proposed to facilitate multi-step rea- |
| applicationofnaturallanguageprocessing(NLP). soning in fact-checking, they either lack explain- |
| Given a claim, the goal is to find evidence and abilityintheirreasoningprocessorrequirealarge |
| then to make a verdict about the claim’s veracity numberoftask-specifictrainingexamples. |
| basedonthatevidence(ThorneandVlachos,2018; Inthispaper,wepresentProgram-GuidedFact- |
| Glockneretal.,2022;Guoetal.,2022). Checking (PROGRAMFC), a novel fact-checking |
| Evaluatingtheveracityofreal-worldclaimsof- framework that is both explanatory and data- |
| teninvolvescollectingmultiplepiecesofevidence efficient. Figure 1 illustrates our approach. To |
| andapplyingcomplexreasoning(Jiangetal.,2020; verifycomplexclaims, PROGRAMFC decomposes |
| Nguyenetal.,2020;AlyandVlachos,2022;Chen themintosimplersub-tasksthatcanbesolvedus- |
| et al., 2022a). For instance, consider the claim ing a shared library of specialized sub-task func- |
| “BothJamesCameronandthedirectorofthefilm tions. Tobespecific, PROGRAMFCbeginsbygen- |
| InterstellarwereborninCanada”. Itmaybechal- erating a reasoning program for the input claim, |
| lenging to find direct evidence on the web that which is a sequence of sub-tasks (e.g., S1-S4 in |
| refutesorsupportsthisclaim. Figure 1) in the form of ACTION[ARGUMENT], |
| 1Theprogramcodeandthedataarepubliclyavailableat |
| where ACTION and ARGUMENT define the type |
| https://github.com/mbzuai-nlp/ProgramFC andthecontentofthesub-task,respectively. |
| 6981 |
| Proceedingsofthe61stAnnualMeetingoftheAssociationforComputationalLinguistics |
| Volume1:LongPapers,pages6981–7004 |
| July9-14,2023©2023AssociationforComputationalLinguistics |
|
|
| Claim: Both James Cameron and the director of the film Interstellar were born in Canada. |
| Knowledge |
| | | | | Reasoning Program | | | Functions | | Source | |
| | --- | --- | --- | ----------------- | --- | --- | --------- | --- | ------ | |
| Language |
| | Models | | S1 Verify | [James Cameron was born in Canada.] | | | | Fact | | |
| | ---------------- | --- | --------- | ----------------------------------- | --- | --- | ------- | ----- | --- | |
| | (Codex, GPT3, …) | | | | | | Checker | | | |
| FACT_1 = TRUE |
| Gold Evidence |
| | | | S2 Question[Who is the director of the film Interstellar?] | | | | | | | |
| | --- | --- | ---------------------------------------------------------- | --- | --- | --- | --- | --- | --- | |
| QA |
| | | | | | ANSWER_1 = Christopher Nolan | | Model | | | |
| | --- | --- | --------- | --------------------------------- | ---------------------------- | --- | ----- | --- | --------- | |
| | | | S3 Verify | [ {ANSWER_1} was born in Canada.] | | | | | Open-book | |
| Fact |
| Claim: ⋯ |
| FACT_2 = FALSE Checker |
| ProCglariamm: :⋯ ⋯ |
| ProCglariamm: :⋯ ⋯ |
| | | | S4 Predict | [ {FACT_1} AND{FACT_2}] | | | | | | |
| | ---------- | --- | ---------- | ----------------------- | --- | --- | -------- | --- | ----------- | |
| | Program: ⋯ | | | | | | Logical | | Closed-book | |
| Reasoner |
| | | | | | PREDICTED_LABEL = | REFUTES | | | | |
| | --- | --- | --- | --- | ----------------- | ------- | --- | --- | --- | |
| Exemplars |
| Figure1: OverviewofourPROGRAMFCmodel,whichconsistsoftwomodules: (i)ProgramGenerationgenerates |
| areasoningprogramfortheinputclaimusingCodexwithin-contextlearning,andthen(ii)ProgramExecution |
| sequentiallyinterpretstheprogrambydelegatingeachsteptothecorrespondingsub-taskfunction. |
| The generated reasoning program serves as a The strategy of program-guided reasoning be- |
| step-by-step guide for verifying the claim. We comesincreasinglyeffectiveastherequiredreason- |
| thenexecutetheprogrambysequentiallydelegat- ingdepthincreases(§4.1). Intheopen-domainset- |
| ing each sub-task to the corresponding sub-task ting,wefindthatreasoningprogramscanenhance |
| handler,asshowninthefunctionscolumnsinFig- theretrievalofrelevantevidencefromknowledge |
| ure 1. These sub-tasks may include answering sources(§4.2). Moreover,PROGRAMFCisrobust |
| questions,verifyingsimpleclaims,orconducting evenwhenweuseweakmodelsassub-tasksolvers |
| logicalreasoning. (§4.2). Wealsoevaluatetheinterpretabilityofthe |
| reasoningprogramsthroughhumanevaluationand |
| PROGRAMFCcombinesexplainabilitywithdata |
| efficiency. Itusesreasoningprogramstoprovide erroranalysis(§4.3). |
| | clear explanations | | of its reasoning | process. | For | | | | | |
| | ------------------ | --- | ---------------- | -------- | --- | --- | --- | --- | --- | |
| 2 RelatedWork |
| | data efficiency, | | Large Language | Models (LLMs) | | | | | | |
| | ---------------- | --- | -------------- | ------------- | --- | --- | --- | --- | --- | |
| can solve various tasks given only a few exam- Fact-Checking. Automated fact-checking has |
| plesasprompts,e.g.,in-contextlearning(Brown |
| | | | | | gained significant | attention | | in the | NLP research | |
| | ------------ | ----------------------------- | --- | --- | ------------------ | --------- | --- | ------ | ------------ | |
| | etal.,2020). | WeleveragethisabilityofLLMsto | | | | | | | | |
| communityinrecentyearsasameansofcombat- |
| generatereasoningprogramsforagivenclaimby ing misinformation and disinformation. Various |
| showingthemodeljustafewdozenof(claim,pro- |
| datasetshavebeenproposedthatenablethedevel- |
| | gram) pairs | as | demonstrations. | PROGRAMFC | is | | | | | |
| | ----------- | --- | --------------- | --------- | --- | --- | --- | --- | --- | |
| opmentandtheevaluationofsystemsforautomatic |
| alsoflexibleasitallowsforeasyswappingofsub- fact-checking,themostpopularonesbeingbased |
| task function implementations to work under dif- on human-crafted claims from Wikipedia con- |
| ferentsettingsoffact-checking,withoutaffecting |
| tent(Thorneetal.,2018;Satheetal.,2020;Schus- |
| | therestofthesystem. | | Wecanallowthefunctions | | | | | | | |
| | ------------------- | --- | ---------------------- | --- | ----------------- | --- | --------- | --------- | ------ | |
| | | | | | ter et al., 2021) | and | naturally | occurring | claims | |
| to retrieve information from external sources (in inthepoliticalorinthescientificdomain(Wang, |
| anopen-booksetting)orwecanaskthemtogen- |
| 2017;Nakovetal.,2021b,2022;Augensteinetal., |
| erateanswersbasedsolelyontheLLM’sinternal |
| | | | | | 2019; Saakyan | et al., | 2021; | Gupta | and Srikumar, | |
| | --- | --- | --- | --- | ------------- | ------- | ----- | ----- | ------------- | |
| parametricknowledge(inaclosed-booksetting). 2021;Waddenetal.,2020,2022a). Notably,most |
| We evaluate PROGRAMFC on two challeng- of these datasets are constructed in a way that |
| ing datasets designed for fact-checking complex the evidence to support or to refute a claim can |
| single |
| claims: HOVER(Jiangetal.,2020)andFEVER- be found in a document. For example, in |
| OUS(Alyetal.,2021),andweshowthatitoutper- FEVER (Thorne et al., 2018), more than 87% of |
| forms seven few-shot fact-checking baselines on theclaimsonlyrequireinformationfromasingle |
| | bothdatasets(§4.1). | | | | Wikipediaarticle(Jiangetal.,2020). | | | | | |
| | ------------------- | --- | --- | --- | ---------------------------------- | --- | --- | --- | --- | |
| 6982 |
| |
| Tobridgethisgap,datasetshavebeenproposed This approach reduces the burden on the lan- |
| tostudyfact-checkingcomplexclaimsthatrequire guage model and allows for more flexibility |
| multi-stepreasoning(Jiangetal.,2020;Alyetal., in incorporating necessary components for fact- |
| 2021). Graph-based models (Zhou et al., 2019; checkingsuchasanevidenceretriever. Thestrat- |
| Liuetal.,2020;Zhongetal.,2020;Nguyenetal., egy of program-guided reasoning is also in line |
| 2020; Barnabò et al., 2022, 2023) are used to fa- withtherecenttrendoftool-augmentedlanguage |
| cilitate the reasoning over multiple pieces of evi- models(Mialon et al., 2023; Schicket al.,2023), |
| dence. Althoughsuchmodelsachievesizableper- i.e., augmenting language models with access to |
| formance gains, they lack explanability and thet externaltoolsandresources. |
| | relyonlargeamountsoftrainingdata. | | | | | Toaddress | | | | | | | | |
| | --------------------------------- | --- | --- | --- | --- | --------- | --- | --- | --- | --- | --- | --- | --- | |
| 3 PROGRAMFC |
| | the above | problems, | we | propose | an explainable, | | | | | | | | | |
| | --------- | --------- | --- | ------- | --------------- | --- | --- | --- | --- | --- | --- | --- | --- | |
| flexible, and data-efficient model that generates Wefirstformulatetheproblemoffact-checkingand |
| | reasoning | graphs | as explanations | | and | utilizes in- | | | | | | | | |
| | --------- | ------ | --------------- | --- | --- | ------------ | --- | --- | --- | --- | --- | --- | --- | |
| thenweintroduceourproposedmodelforProgram- |
| contextlearningtoenablefew-shotlearning. |
| GuidedFact-Checking(PROGRAMFC). |
| ExplanationGeneration. Facingthecomplex- 3.1 ProblemFormulation |
| | ities of real-world | | claims, | simply | giving | a final | | | | | | | | |
| | ------------------- | --- | ------- | ------ | ------ | ------- | -------------------------------- | --- | --- | --- | --- | ------ | --- | |
| | | | | | | | GivenaclaimC,afact-checkingmodel | | | | | aimsto | | |
| F |
| veracitytoaclaimoftenfailstobepersuasive(Guo predictalabelY toevaluatetheclaimasTRUEor |
| | et al., 2022). | | Previous | research | has | proposed | | | | | | | | |
| | ------------------ | ----- | ------------ | -------- | -------- | -------- | ------------------------------------ | --------- | -------- | ------ | --- | ---------- | ------ | |
| | | | | | | | FALSE,basedonaknowledgesource | | | | | . Themodel | | |
| | various approaches | | to | provide | post-hoc | explana- | | | | | | K | | |
| | | | | | | | isalsorequiredtooutputanexplanationE | | | | | | tojus- | |
| | tions for | model | predictions, | such | as using | atten- | | | | | | | | |
| | | | | | | | tify the | predicted | veracity | label. | We | summarize | | |
| tion weights to highlight relevant parts of the ev- threedifferentsettingsoffact-checkingdepending |
| | idence (Popat | et | al., 2017; | Cui | et al., 2019; | Yang | | | | | | | | |
| | ------------- | --- | ---------- | --- | ------------- | ---- | -------------------------- | --- | --- | --- | --- | --- | --- | |
| | | | | | | | onthetypeofknowledgesource | | | | . | | | |
| K |
| | et al., 2019; | Lu | and Li, | 2020), | generating | justifi- | | | | | | | | |
| | ------------------------------------------ | --- | ------- | ------ | ---------- | -------- | ------- | --------- | --------- | ---- | -------- | ------- | --- | |
| | | | | | | | Gold | evidence: | For | each | claim, | is the | set | |
| | cationswithlogic-basedsystemsbasedonknowl- | | | | | | • | | | | | K | | |
| | | | | | | | of gold | evidence | documents | | that can | support | or | |
| edgegraphs(Gad-Elrabetal.,2019;Ahmadietal., |
| | | | | | | | refutetheclaim. | | Thissettingisalsocalledclaim | | | | | |
| | --- | --- | --- | --- | --- | --- | --------------- | --- | ---------------------------- | --- | --- | --- | --- | |
| 2019),andgeneratingasummaryoftheretrieved |
| verification(Panetal.,2021;Wrightetal.,2022). |
| relevantevidence(Atanasovaetal.,2020;Kotonya |
| | | | | | | | Open-booksetting: | | | isalargetextualcorpus | | | | |
| | ------------------------------ | --- | --- | --- | ------------- | --- | ----------------- | --- | --------------------------- | --------------------- | --- | --- | --- | |
| | andToni,2020;Jollyetal.,2022). | | | | Incontrast,we | | | | | | | | | |
| | | | | | | | • | | | K | | | | |
| | | | | | | | suchasWikipedia. | | Themodelfirstretrievesrele- | | | | | |
| proposetousereasoningprogramstoprovideex- |
| vantevidencefromthecorpusandthenpredictsthe |
| planationsthatconsistofsub-tasksdescribedina |
| | | | | | | | veracity | label | based | on the evidence | | (Jiang | et al., | |
| | ---------------------------- | --- | --- | --- | ----------------- | --- | -------- | ----- | ----- | --------------- | --- | ------ | ------- | |
| | program-likenaturallanguage. | | | | Thisoffersseveral | | | | | | | | | |
| 2021;Waddenetal.,2022b). |
| | advantages: | itallowsforexplanationsthatarenot | | | | | | | | | | | | |
| | --------------------------------------------- | --------------------------------- | --- | --- | --- | --- | ----------------------------------- | ----------- | --- | ------------------- | --- | ------ | ------ | |
| | | | | | | | Closed-booksetting: | | | Themodeldoesnothave | | | | |
| | confinedtotheevidence,likeattentionweights,it | | | | | | • | | | | | | | |
| | | | | | | | accesstoanyexternalknowledgesource( | | | | | | = ). | |
| | ismoreflexiblethanlogic-basedexplanations,and | | | | | | | | | | | K | ∅ | |
| | | | | | | | It needs | to leverage | | the knowledge | | stored | in its | |
| itismoreconcisethanfree-formsummarization. |
| parameters(acquiredduringpre-trainingandfine- |
| | | | | | | | tuning) | to verify | the | claim. This | setting | was | ex- | |
| | ---------------- | --- | ---------- | --- | --------- | --- | ------- | --------- | --- | ----------- | ------- | --- | --- | |
| | Chain-of-Thought | | Reasoning. | | Moreover, | un- | | | | | | | | |
| ploredinworkthatapplieslargelanguagemodels |
| likepreviousworkthatgeneratespost-hocexplana- |
| forfact-checking(Leeetal.,2020,2021). |
| tions,wealsousereasoningprogramsasguidance |
| forpredictingtheveracityoftheclaim. Thisismo- 3.2 Program-GuidedReasoning |
| | tivated by | the recent | success | of | chain-of-thought | | | | | | | | | |
| | ---------- | ---------- | ------- | --- | ---------------- | --- | ----------------------------------- | --- | --- | --- | --- | --- | ---- | |
| | | | | | | | Ourgoalistofact-checkacomplexclaimC | | | | | | that | |
| prompting(CoT)(Weietal.,2022;Kojimaetal., |
| Wefocusonthefew- |
| requiresmulti-stepreasoning. |
| 2022;Wangetal.,2022),whichgeneratesstep-by- |
| | | | | | | | shot setting, | whereonlyasmallsetofin-domain | | | | | | |
| | --- | --- | --- | --- | --- | --- | ------------- | ----------------------------- | --- | --- | --- | --- | --- | |
| stepnaturallanguagereasoningstepstoguidethe |
| | | | | | | | examplesareavailabletoteachthemodel. | | | | | Tosolve | | |
| | --------------------------------- | --- | --- | --- | --- | ------- | ------------------------------------ | --- | --- | --- | --- | ------- | --- | |
| | modelinansweringcomplexquestions. | | | | | Weadopt | | | | | | | | |
| this,PROGRAMFCfollowsaprogramgeneration- |
| | thisideatofact-checkingcomplexclaims. | | | | | Unlike | | | | | | | | |
| | ------------------------------------- | --- | --- | --- | --- | ------ | --- | --- | --- | --- | --- | --- | --- | |
| and-executionparadigm,asshowninFigure1. |
| theoriginalCoT,whichusesasingleLLMforboth |
| decompositionandquestionanswering,weusethe Program Generation. At this stage, given the |
| language model only to generate reasoning pro- inputclaimC,aplanner generatesareasoning |
| P |
| gramsastheblueprintforproblem-solving,andwe program P = [S , ,S ] for it, which consists |
| | | | | | | | | | 1 ··· | n | | | | |
| | --- | --- | --- | --- | --- | --- | --- | --- | ----- | --- | --- | --- | --- | |
| delegateeachsub-tasktospecializedfunctions. ofnsequentiallyorderedreasoningstepsS i . |
| 6983 |
| |
| Each reasoning step S i P is an instruction However,thegrammarofareasoningprogram |
| ∈ |
| S |
| in controlled natural language that directs i to is different from the grammar of a programming |
| a function in an auxiliary set of sub-task func- language. WetakeadvantageofCodex’sfew-shot |
| tions available to the system. To be specific, generalizationabilityandwefindthatitcanlearn |
| F |
| we define S i = (f i ,A i ,V i ), where f i specifies effectivelyfromonlyasmallnumberofin-context |
| thesub-taskfunctionf , A istheargument examples = d , ,d . Each example d |
| | | | | i | i | | | | | | 1 | D | | | i | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ---- | --- | --- | --- | |
| | | | | ∈ F | | | | | D | { | ··· | | |} | | | | |
| passed to the function f , and V is the variable consistsofaclaimandaprogram. Theprogramhas |
| | | | | i | i | | | | | | | | | | | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| thatstoresthereturnedresultfromthefunctioncall aPython-likegrammar,whereeachreasoningstep |
| f (A ). Foravalidreasoningprogram,thereturn iswrittenintheformatV = f (A ). Atinference |
| | i i | | | | | | | | | | | i i | i | | | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| valueofthelastreasoningstepmustbeaBoolean time,wepromptCodexwithaninstructionofthe |
| valueindicatingtheveracitylabeloftheclaimC, task,K in-contextexamples,andtheinputclaim |
| i.e.,V n TRUE,FALSE . C. Codex then attempts to complete the follow- |
| | | ∈ { | | } | | | | | | | | | | | | |
| | ----------------- | -------- | ----- | ----------------------- | --------- | ----------- | ------- | ----------------------------------------- | -------------------------------- | --- | --- | --- | --- | ----- | --- | |
| | | | | | | | | ingtexts, | andtherebygeneratesaprogramforC. | | | | | | | |
| | ProgramExecution. | | | Intheexecutionstage,the | | | | | | | | | | | | |
| | | | | | | | | TheprompttemplateisshowninFigure2. | | | | | | Weuse | | |
| | reasoning | program | P | is run | by an | interpreter | to | | | | | | | | | |
| | | | | | | | | K = 20tomaintainatradeoffbetweenthediver- | | | | | | | | |
| | derive the | veracity | label | of | the claim | C. | The in- | | | | | | | | | |
| sityofreasoningtypesandthemodel’smaximum |
| terpretersequentiallyparsesthereasoningstepsin |
| | | | | | | | | input | capacity. | Weuse | sampling-based | | | decoding | | |
| | --------------- | --- | --- | ------- | ------------------- | --- | --- | ----- | --------- | ----- | -------------- | --- | --- | -------- | --- | |
| | P. ForeachstepS | | | = (f ,A | ,V ),itcallsthecor- | | | | | | | | | | | |
| | | | i | i | i i | | | | | | | | | | | |
| (temperatureof0.7)togeneratedifferentreasoning |
| | responding | off-the-shelf | | sub-task | function | | f and | | | | | | | | | |
| | ---------- | ------------- | --- | -------- | -------- | --- | ----- | --- | --- | --- | --- | --- | --- | --- | --- | |
| i |
| programsformultipleruns. |
| | passestheargumentA | | | toit. | TheargumentA | | isei- | | | | | | | | | |
| | ------------------ | --- | --- | ----- | ------------ | --- | ----- | --- | --- | --- | --- | --- | --- | --- | --- | |
| | | | | i | | | i | | | | | | | | | |
| theralogicalexpressionoranaturallanguagesen- 3.4 Sub-TaskFunctions |
| | tence,e.g.,aquestionorasimpleclaim. | | | | | Theresult | | | | | | | | | | |
| | ------------------------------------------- | ------ | ---- | ------------ | ------ | --------- | ------ | ------------------------------------- | --- | -------------------------------- | -------- | --- | --------- | --- | --- | |
| | | | | | | | | We implement | | three | sub-task | | functions | for | the | |
| | ofthefunctioncallisthenstoredinthevariableV | | | | | | | . | | | | | | | | |
| | | | | | | | i | modeltocallduringtheprogramexecution. | | | | | | | | |
| | As it is | common | for | a subsequent | step | to | depend | | | | | | | | | |
| | | | | | | | | QUESTION: | | Thissub-taskfunctionisaquestion- | | | | | | |
| | on the results | | from | previous | steps, | we allow | the | | | | | | | | | |
| • |
| | | | | | | | | answering | module | | that takes | a | question | Q as | the | |
| | -------------- | --------------------------------- | ----------------------------- | ------------ | --- | ---- | --- | --------- | -------------------------------- | --- | ---------- | --- | -------- | ---- | --- | |
| | argument | A to | refer | to variables | V | , ,V | in | | | | | | | | | |
| | | i | | | | 1 | i 1 | | | | | | | | | |
| | | | | | | ··· | − | input | argument | and | returns | the | answer | A to | the | |
| | previoussteps. | | Forexample,inFigure1,theargu- | | | | | | | | | | | | | |
| | | | | | | | | question. | WeuseFLAN-T5(Chungetal.,2022),an | | | | | | | |
| | mentinS | 3 is“{ANSWER_1}wasborninCanada.”, | | | | | | | | | | | | | | |
| improvedT5model(Raffeletal.,2020)pretrained |
| | which refers | to | the return | variable | | {ANSWER_1} | | | | | | | | | | |
| | ------------ | ------ | ---------- | -------- | ------- | ---------- | ------ | ------- | ---- | ---- | ----- | ---------------- | --- | ------- | --- | |
| | | | | | | | | on more | than | 1.8K | tasks | with instruction | | tuning, | | |
| | from S | . When | executing | | S , the | variable | is re- | | | | | | | | | |
| | 2 | | | | 3 | | | | | | | | | | | |
| whichhasachievedstate-of-the-artzero/few-shot |
| | placed | by its | actual | value, | and the | argument | be- | | | | | | | | | |
| | ------ | ------ | ------ | ------ | ------- | -------- | --- | ------------------------------ | --- | --- | --- | --- | --- | ------- | --- | |
| | | | | | | | | performanceonmanyQAbenchmarks. | | | | | | Asshown | | |
| comes“ChristopherNolanwasborninCanada”. |
| | | | | | | | | in Figure | 3, | we prompt | | the model | differently | | de- | |
| | --- | --- | --- | --- | --- | --- | --- | --------- | --- | --------- | --- | --------- | ----------- | --- | --- | |
| Afterexecutingthelaststep,thereturnvalueisthe |
| | | | | | | | | pendingonthesettingsdefinedinSection3.1. | | | | | | | For | |
| | --- | --- | --- | --- | --- | --- | --- | ---------------------------------------- | --- | --- | --- | --- | --- | --- | --- | |
| predictedveracityoftheclaimC. |
| theclosed-booksetting,theinputpromptis |
| | AggregatingReasoningPaths. | | | | Notethatthere | | | | | | | | | | | |
| | -------------------------- | -------- | --------- | --- | ------------- | -------- | ----- | --- | --------- | --- | --- | ------ | --- | --- | --- | |
| | | | | | | | | Q: | QUESTION? | | The | answer | is: | | | |
| | might be | multiple | reasoning | | paths | that can | reach | | | | | | | | | |
| Fortheothertwosettings,theinputpromptis |
| | the final | veracity | label. | Therefore, | | we generate | | | | | | | | | | |
| | --------- | -------- | ------ | ---------- | --- | ----------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| a diverse set of N candidate reasoning programs EVIDENCE Q: QUESTION? |
| | | | | | | | | The | answer | | is: | | | | | |
| | ------------------- | ------ | ------------------- | ---------------------- | --- | --------- | --- | ------- | ------ | --------------------------------- | --- | --- | --- | --- | --- | |
| | = P | 1 , ,P | N fortheinputclaim. | | | Afterexe- | | | | | | | | | | |
| | P { | ··· | } | | | | | | | | | | | | | |
| | cutingallprogramsin | | | ,wetakethemajorityvote | | | | | | | | | | | | |
| | | | | | | | | VERIFY: | | Thisisafactverificationmodulethat | | | | | | |
| P |
| | overallN | predictedlabelsasthefinallabel. | | | | | This | • | | | | | | | | |
| | -------- | ------------------------------- | --- | --- | --- | --- | ---- | ------------ | --- | ---------------------------- | --- | --- | --- | --- | --- | |
| | | | | | | | | takesaclaimC | | astheinputargumentandreturns | | | | | | |
| approachissimilartohowhumansrelyonmultiple |
| | | | | | | | | a label | of either | TRUE | | or FALSE. | We | also | use | |
| | --- | --- | --- | --- | --- | --- | --- | ------- | --------- | ---- | --- | --------- | --- | ---- | --- | |
| methodsofvalidationtoincreasetheirconfidence FLAN-T5forthismodule,bypromptingthemodel |
| | infact-checking. | | Italsomakesthemodellesssus- | | | | | | | | | | | | | |
| | ---------------- | --- | --------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| withthefollowingquestion-answeringformat. |
| ceptibletoerrorsinindividualreasoningprograms. |
| EVIDENCE |
| | 3.3 ReasoningProgramGeneration | | | | | | | Q: | Is | it true | that | CLAIM? | | | | |
| | ------------------------------ | ----------- | --- | --------- | --- | ----- | ----- | ---- | --- | ------- | ---- | ------ | --- | --- | --- | |
| | | | | | | | | True | or | False? | The | answer | is: | | | |
| | We base | our program | | generator | on | Codex | (Chen | | | | | | | | | |
| et al., 2021), a code-pretrained LLM, which can PREDICT: Thismoduletakesasinputalogical |
| • |
| parse natural language into symbolic representa- expressionthatperformsAND,OR,NOToperations |
| tionssuchasSQL(Chengetal.,2022)orPython overthevariablesintheprevioussteps. Itsoutput |
| programs(Gaoetal.,2022;Chenetal.,2022b). isreturnedasthepredictedveracitylabel. |
| 6984 |
| |
| '''Generate a python-like program that describes the reasoning steps |
| required to verify the claim step-by-step. You can call three functions |
| in the program: 1. Question() to answer a question; 2. Verify() to |
| verify a simple claim; 3. Predict() to predict the veracity label.''' |
| # The claim is that Both James Cameron and the director of the film |
| | | Interstellar | | were | born | in | Canada. | | | | | | | |
| | --- | ------------ | --- | ------------- | ---- | ------- | ------- | ---- | --- | --------- | --- | --- | --- | |
| | def | program(): | | | | | | | | | | | | |
| | | fact_1 | = | Verify("James | | Cameron | was | born | in | Canada.") | | | | |
| Answer_1 = Question("Who is the director of the film Interstellar?") |
| | | fact_2 | = | Verify("{Answer_1} | | | was | born | in Canada.") | | | | | |
| | --- | ---------- | ---------------- | ------------------ | ------------- | ---- | ------- | ---- | ------------ | --- | --- | --- | --- | |
| | | label | = Predict(fact_1 | | | and | fact_2) | | | | | | | |
| | ( | more | in-context | examples | | here | ) | | | | | | | |
| | | ··· | | | | | ··· | | | | | | | |
| | # | The claim | is | that | <input_claim> | | | | | | | | | |
| | def | program(): | | | | | | | | | | | | |
| Figure 2: The Codex prompt template used to generate reasoning programs, consisting of a task instruction, |
| in-contextexamples,andapromptforthe<input_claim>. ThefulltemplatesaregiveninAppendixD. |
| <Gold Evidence> |
| Forevaluationintheopen-booksetting,weuse |
| | Gold Evidence | | Q: <Question> | | | | | | | | | | | |
| | ------------- | --- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| The answer is: the corresponding Wikipedia corpus constructed |
| | | Open-book | | | | | | for | these | two datasets | as the knowledge | sources. | | |
| | --- | --------- | --- | --- | --- | --- | --- | --- | ----- | ------------ | ---------------- | -------- | --- | |
| <Retrieved Evidence> |
| <Question> Q: <Question> FLAN-T5 Ans HOVER uses the October 2017 Wikipedia dump |
| The answer is: |
| | | Retriever | | | | | | processed | | by Yang | et al. (2018), | consisting | of | |
| | --- | --------- | --- | --- | --- | --- | --- | --------- | --- | ------- | -------------- | ---------- | --- | |
| theintroductorysectionsof5.2millionWikipedia |
| | Closed-book | | Q: <Question> | | | | | | | | | | | |
| | ----------- | --- | ------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| The answer is: |
| | | | | | | | | pages. | FEVEROUSusestheDecember2020dump, | | | | | |
| | --- | --- | --- | --- | --- | --- | --- | ------ | -------------------------------- | --- | --- | --- | --- | |
| Figure 3: Implementation of the question-answering including5.4millionfullWikipediaarticles. |
| sub-taskfunctionforthreedifferentsettings. |
| | | | | | | | | Baselines. | | Wecompare | PROGRAMFC | toseven | | |
| | ------------- | ------------------------------------ | --- | --- | --- | --- | --- | -------------- | -------- | ---------------------------- | --------------- | ------------- | -------- | |
| | | | | | | | | baselines, | | categorized | into three | groups. | (i) Pre- | |
| | 4 Experiments | | | | | | | trainedmodels: | | BERT-FC(Soleimanietal.,2020) | | | | |
| | | | | | | | | and | LisT5 | (Jiang | et al., 2021) | are two | models | |
| | Datasets. | Mostfact-checkingdatasetsconsistpri- | | | | | | | | | | | | |
| | | | | | | | | that | leverage | BERT | and T5 for fact | verification, | | |
| marilyofsimpleclaimsthatcanbesubstantiated |
| | | | | | | | | respectively. | | (ii) FC/NLI | fine-tuned | models: | we | |
| | ------------------------------ | --- | --- | --- | --- | ------------ | --- | ------------- | --- | ----------- | ---------- | ------- | --- | |
| | throughasinglepieceofevidence. | | | | | However,here | | | | | | | | |
| choosethreepretrainedmodelsthatarefine-tuned |
| | wefocusoncomplexclaimsthatneedmulti-step | | | | | | | | | | 1 | | | |
| | ---------------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| onotherfact-checkingdatasetsornaturallanguage |
| | reasoning. | Given | this | context, | we | opt | to evalu- | | | | | | | |
| | ---------- | ----- | ---- | -------- | --- | --- | --------- | ----------------------- | --- | --- | --------------------- | --- | --- | |
| | | | | | | | | inference(NLI)datasets. | | | RoBERTa-NLI(Nieetal., | | | |
| ate our model on the only two datasets that, to 2020)usesfine-tunedRoBERTa-largeonfourNLI |
| | the best | of our | knowledge, | | fulfill | these | criteria: | | | | | | | |
| | -------- | ------ | ---------- | --- | ------- | ----- | --------- | --------- | --- | ------------- | ------ | ---------- | ----- | |
| | | | | | | | | datasets; | | DeBERTaV3-NLI | (He et | al., 2021) | fine- | |
| HOVER(Jiangetal.,2020)andFEVEROUS(Aly |
| tunestheDeBERTaV3modelon885,242(claim, |
| | etal.,2021). | Weusethevalidationsetsforevalu- | | | | | | | | | | | | |
| | ------------ | ------------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| evidence,label)annotationsfromFEVERandfour |
| | ation | since the | test sets | are | not publicly | | released. | | | | | | | |
| | ----- | --------- | --------- | ---- | ------------ | ----------- | --------- | --- | --------- | --------- | ------- | ------- | ------ | |
| | | | | | | | | NLI | datasets. | MULTIVERS | (Wadden | et al., | 2022b) | |
| | HOVER | contains | claims | that | require | integration | | | | | | | | |
| isaLongFormer(Beltagyetal.,2020)modelfine- |
| | andreasoningovermultipleWikipediaarticles. | | | | | | We | | | | | | | |
| | ------------------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| tunedonFEVER.(iii)In-contextlearningmodels: |
| divideitsvalidationsetintothreesubsetsbasedon one baseline is that we directly use the FLAN-T5 |
| thenumberof“hops”requiredtoverifytheclaim: |
| | | | | | | | | modelinourVERIFY | | | moduleforfact-checking. | | | |
| | --- | --- | --- | --- | --- | --- | --- | ---------------- | --- | --- | ----------------------- | --- | --- | |
| 1,126two-hopclaims,1,835three-hopclaims,and |
| Theotherbaselineusesthein-contextlearningof |
| 1,039 four-hop claims. FEVEROUS focuses on Codexforfew-shotfact-checking. |
| Theimplemen- |
| | fact-checking | | complex | claims | over | unstructured | | | | | | | | |
| | ------------- | --- | ------- | ------ | ---- | ------------ | --- | --- | --- | --- | --- | --- | --- | |
| tationdetailsaregiveninAppendixA. |
| andstructureddata,whereeachclaimisannotated |
| withevidenceintheformofsentencesand/orcells Few-ShotLearning. Westudyfew-shotlearning |
| fromtablesinWikipedia. Sincewefocusontextual whereonlyafewin-domainexamplesareavailable. |
| fact-checking,weonlyselectedclaimsthatrequire Therefore, for a fair comparison, we restrict all |
| exclusivelysentenceevidence,constituting2,962 models to have access to only 20 examples from |
| | claims. | WecallthissubsetFEVEROUS-S. | | | | | | HOVERorFEVEROUS-S. | | | | | | |
| | ------- | --------------------------- | --- | --- | --- | --- | --- | ------------------ | --- | --- | --- | --- | --- | |
| 6985 |
| |
| | | | HOVER(2-hop) | HOVER(3-hop) | HOVER(4-hop) | FEVEROUS-S | | |
| | --- | --- | ------------ | ------------ | ------------ | ---------- | --- | |
| Few-shotlearningmodels |
| | | | Gold Open | Gold | Open Gold | Open Gold | Open | |
| | --- | --- | --------- | ---- | --------- | --------- | ---- | |
| BERT-FC(Soleimanietal.,2020) 53.40 50.68 50.90 49.86 50.86 48.57 74.71 51.67 |
| I |
| LisT5(Jiangetal.,2021) 56.15 52.56 53.76 51.89 51.67 50.46 77.88 54.15 |
| RoBERTa-NLI(Nieetal.,2020) 74.62 63.62 62.23 53.99 57.98 52.40 88.28 57.80 |
| II DeBERTaV3-NLI(Heetal.,2021) 77.22 68.72 65.98 60.76 60.49 56.00 91.98 58.81 |
| MULTIVERS(Waddenetal.,2022b) 68.86 60.17 59.87 52.55 55.67 51.86 86.03 56.61 |
| Codex(Chenetal.,2021) |
| | | | 70.63 65.07 | 66.46 | 56.63 63.49 | 57.27 89.77 | 62.58 | |
| | --- | --- | ----------- | ----- | ----------- | ----------- | ----- | |
| III |
| FLAN-T5(Chungetal.,2022) 73.69 69.02 65.66 60.23 58.08 55.42 90.81 63.73 |
| | ProgramFC | (N=1) | | | | | | |
| | --------- | ----- | ----------- | ----- | ----------- | ----------- | ----- | |
| | | | 74.10 69.36 | 66.13 | 60.63 65.69 | 59.16 91.77 | 67.80 | |
| IV |
| ProgramFC (N=5) 75.65 70.30 68.48 63.43 66.75 57.74 92.69 68.06 |
| Table 1: Macro-F1 scores of PROGRAMFC (IV) and baselines (I-III) on the evaluation set of HOVER and |
| FEVEROUS-Sforfew-shotfact-checking. GoldandOpenrepresentthegoldevidencesettingandtheopenbook |
| setting,respectively. I:pretrainedTransformers;II:FC/NLIfine-tunedmodels;III:in-contextlearningmodels. |
| We use these examples either for fine-tuning However,thisgeneralizationbecomesmorechal- |
| pre-trainedmodels(BERT-FCandLisT5),forcon- lengingasthecomplexityoftheclaimsincreases. |
| tinuousfine-tuningtheFC/NLIfine-tunedmodels, OnHOVER,theF1scoreofDeBERTaV3-NLIdrops |
| orasin-contextexamplesforFLAN-T5andCodex. |
| | | | | from 77.22 | for 2-hop claims | to 60.49 | for 4-hop | |
| | --- | --- | --- | ---------- | ---------------- | -------- | --------- | |
| For PROGRAMFC,weusethemasin-contextex- claims,whichisadecreaseof21.7%. Incontrast, |
| amplesforreasoningprogramgeneration. theperformancedropforProgramFC,whichuses |
| Weevaluateboththegoldevidencesettingand thestrategyofprogram-guidedreasoning,ismuch |
| smaller: just11.7%. |
| theopen-booksetting. Thebaselinemodelsarethe |
| | same for | both settings. However, | during testing | | | | | |
| | -------- | ----------------------- | -------------- | ------------- | ------- | -------------- | -------- | |
| | | | | Decomposition | is more | effective than | one-step | |
| intheopen-booksetting,themodelsaregiventhe |
| | | | | prediction. | The ProgramFC | model, which | uses | |
| | --- | --- | --- | ----------- | ------------- | ------------ | ---- | |
| retrievedevidenceratherthantheground-truthev- |
| | | | | the same | FLAN-T5 model | as the sub-task | func- | |
| | ------- | ---------------------- | ------------- | -------- | ------------- | --------------- | ----- | |
| | idence. | We use BM25 (Robertson | and Zaragoza, | | | | | |
| tions, outperformsthebaselineofdirectlyverify- |
| 2009)implementedwiththePyserinitoolkit(Lin |
| | | | | ingclaimswithFLAN-T5onallfourdatasets. | | | On | |
| | --- | --- | --- | -------------------------------------- | --- | --- | --- | |
| etal.,2021)astheretrieverforbothPROGRAMFC |
| average,thereisa6.0%improvementinthegold |
| | andthebaselines. | Weuseasevidencethetop-10 | | | | | | |
| | ---------------- | ------------------------ | --- | -------- | ------------------ | ----------- | ------ | |
| | | | | evidence | setting and a 4.5% | improvement | in the | |
| paragraphsretrievedfromtheknowledgecorpus. |
| | | | | open-booksetting. | Thissuggeststhatdecomposing | | | |
| | --- | --- | --- | ----------------- | --------------------------- | --- | --- | |
| acomplexclaimintosimplerstepswithaprogram |
| 4.1 MainResults |
| | | | | canfacilitatemoreaccuratereasoning. | | | Thisises- | |
| | --- | --- | --- | ----------------------------------- | --- | --- | --------- | |
| WereporttheoverallresultsforPROGRAMFCand pecially evident when the required reasoning is |
| forthebaselinesforfew-shotfact-checkinginTa- complex: thereisa14.9%improvementinthegold |
| ble1. PROGRAMFCachievesthebestperformance |
| | | | | evidence | setting and a 6.7% | improvement | in the | |
| | --- | --- | --- | -------- | ------------------ | ----------- | ------ | |
| on7outof8evaluations,demonstratingitseffec- open-booksettingfor4-hopclaims. |
| | tiveness. | Wehavethreemorespecificobservations. | | | | | | |
| | --------- | ------------------------------------ | --- | --- | --- | --- | --- | |
| Aggregatingreasoningprogramsishelpful. |
| ProgramFCismoreeffectiveondeeperclaims. |
| On the HOVER dataset, ProgramFC (N=5) out- WefindthataggregatingthepredictionsofN = 5 |
| performs the baselines on average by 10.38%, reasoningprogramsimprovestheperformanceover |
| 11.37%, and 14.77% on two-hop, three-hop, and using a single program by an average of 1.5%. |
| four-hop claims, respectively. This suggests that ThisalignswiththefindingsofWangetal.(2022), |
| ProgramFCbecomesincreasinglyeffectiveasthe wheretheideawasappliedforquestionanswering: |
| required reasoning depth increases. Among the if multiple different ways of thinking lead to the |
| DeBERTaV3-NLI |
| baselines, performs comparably sameanswer,wecanhavegreaterconfidencethat |
| to ProgramFC on two-hop claims, indicating that thefinalansweriscorrect. Thisintuitionalsoap- |
| large-scalepre-trainingonsimplerclaimscanhelp pliestofact-checking,aseachprogramrepresents |
| themodelgeneralizetomorecomplexclaims. auniquereasoningchaintoverifytheclaim. |
| 6986 |
| |
| FLAN-T5 ProgramFC HOVER (2-hop) FLAN-T5 ProgramFC HOVER (3-hop) FLAN-T5 ProgramFC HOVER (4-hop) |
| | 80 | | 76.11 | 75.65 | 77.62 | 80 | | | 80 | | | | |
| | --- | --- | ----- | ----- | ----- | --- | --- | --- | --- | --- | --- | --- | |
| 72.56 |
| 77.07 |
| | 70 | | | 73.69 | | 70 67.88 | 68.55 68.48 | 69.56 | 70 | 68.37 | 68.56 | 68.18 | |
| | ----- | ----- | ----- | ----- | --- | -------- | ----------- | ----- | ----- | ----- | ----- | ----- | |
| | 64.35 | | 71.69 | | | | | | | | 66.75 | | |
| | | 68.24 | | | | 62.23 | | | 62.46 | | | | |
| | | | | | | | 65.07 65.66 | 66.89 | | | | | |
| | 60 | | | | | 60 63.05 | | | 60 | | | 63.39 | |
| 61.36 |
| 58.08 |
| 56.58 |
| | 50 | | | | | 50 | | | 50 | | | | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| 49.29 |
| | 47.75 | | | | | | | | 48.59 | | | | |
| | ----- | --- | --- | --- | --- | --- | --- | --- | ----- | --- | --- | --- | |
| | 40 | | | | | 40 | | | 40 | | | | |
| 80M 250M 780M 3B 11B 80M 250M 780M 3B 11B 80M 250M 780M 3B 11B |
| F1scoreforfact-checkingwithgoldevidenceusingFLAN-T5(blueline)andPROGRAMFC(greenline) |
| Figure4: |
| forlanguagemodelsofincreasingsizes: FLAN-T5-small(80M),FLAN-T5-base(250M),FLAN-large(780M), |
| FLAN-T5-XL(3B),andFLAN-T5-XXL(11B)onHOVER2-hop(left),3-hop(middle),and4-hop(right). |
| 1 |
| | 90 | | | | | | We | measure | the recall | of the | gold paragraphs | | |
| | --- | ----- | ------------------ | --- | --------- | ----- | ------- | ------- | ---------- | ---------- | --------------- | --- | |
| | | | One-step Retrieval | | ProgramFC | | | | | | | | |
| | 80 | | | | | 85.65 | | | | | | | |
| | | | | | | | for the | top-10 | retrieved | paragraphs | (recall@10). | | |
| | | 77.13 | | | | 76.25 | | | | | | | |
| 70 73.18 |
| For PROGRAMFC,wecombinetheretrievedpara- |
| 60 |
| graphsofallstepsandweconsiderthetop-10re- |
| 59.17 |
| 50 |
| | | | 51.33 | | 49.93 | | sults. | We can | see in | Figure 5 that | PROGRAMFC | | |
| | --- | --- | ----- | --- | ----- | --- | ------ | ------ | ------ | ------------- | --------- | --- | |
| 40 |
| outperformsone-stepretrievalonalldatasets,with |
| | 30 | | | | 36.43 | | | | | | | | |
| | --- | --- | --- | --- | ----- | --- | ----------- | ----------- | --- | -------- | -------- | --- | |
| | | | | | | | the largest | improvement | | of 37.1% | on HOVER | 4- | |
| 20 |
| HOVER (2-hop) HOVER (3-hop) HOVER (4-hop) FEVEROUS-S hop. Thisisbecausesomeinformationmaynotbe |
| presentintheoriginalclaim,butisonlyrevealed |
| Figure5: Retrievalrecall@10fortheone-stepretrieval |
| andtheiterativeretrievalinPROGRAMFC. during the reasoning process (e.g., “Christopher |
| | | | | | | | Nolan”inFigure1). | | Thus,iterativeretrievalguided | | | | |
| | --- | --- | --- | --- | --- | --- | ----------------- | --- | ----------------------------- | --- | --- | --- | |
| bythereasoningprogramyieldsbetterresults. |
| 4.2 HowDoestheReasoningProgramHelp? |
| 4.3 InterpretabilityofReasoningPrograms |
| | To further | understand | | how | reasoning | programs | | | | | | | |
| | ---------- | ---------- | --- | --- | --------- | -------- | --- | --- | --- | --- | --- | --- | |
| facilitate fact-checking, we compare the perfor- Anadvantageof PROGRAMFC isthatitimproves |
| FLAN-T5 |
| mance of PROGRAMFC with using dif- the interpretability of fact-checking compared to |
| | ferentlanguagemodelsizes: | | | | small,base,large, | | | | | | | | |
| | ------------------------- | --- | --- | --- | ----------------- | --- | --- | --- | --- | --- | --- | --- | |
| end-to-endmodels,astheexplicitprogramcanaid |
| XL, and XXL. The results are shown in Figure 4 human understanding and debugging. Examples |
| andindicatethatprogram-guidedreasoningispar- ofgeneratedreasoningprogramscanbefoundin |
| | ticularly | effective | when | the | model | size is small. | | | | | | | |
| | --------- | --------- | ---- | --- | ----- | -------------- | ------ | ------------- | --- | ------------ | ----------- | --- | |
| | | | | | | | Figure | 7 of Appendix | | B. To assess | the quality | of | |
| As smaller models have less capacity for com- thegeneratedreasoningprograms,wesampled300 |
| plexreasoning,theperformanceoftheend-to-end claimswhere PROGRAMFC incorrectlypredicted |
| FLAN-T5 |
| model decreases significantly with de- the final veracity labels from the HOVER 2-hop, |
| creasing model size. However, this trend is less 3-hop,and4-hopdatasets,with100examplesper |
| notableforPROGRAMFC.Thehigh-levelreason- dataset. Weaskedhumanannotatorstoanalyzethe |
| ing plan offered by reasoning programs substan- errortypesandweclassifiedtheresultsintothree |
| tiallyalleviatesthedemandsonthesubsequentsub- |
| | | | | | | | categories: | (i)Syntacticerrors,wheretheprogram | | | | | |
| | --- | --- | --- | --- | --- | --- | ----------- | ---------------------------------- | --- | --- | --- | --- | |
| task solvers. Our results show that the program- doesnotconformtothedefinedgrammarandcan- |
| guidedmodelusingFLAN-T5-small(80Mparam- notbeparsed,(ii)Semanticerrors,whichinclude |
| eters)assub-tasksolverscanachievecomparable |
| incorrectormissingarguments/variables(Token), |
| performancetothe137xlargerFLAN-T5-XXL(11B) incorrectprogramstructure(Structure),andincor- |
| modelwithend-to-endreasoningfor4-hopclaims. rect sub-task calls (Subtask), and (iii) Incorrect |
| Intheopen-domainsetting,wefindthatreason- execution,wheretheprogramiscorrect,butwhere |
| ingprogramscanenhancetheretrievalofrelevant theincorrectpredictionisaresultofitsexecution. |
| evidence from the knowledge source. Figure 5 We show the error analysis in Table 2. First, |
| comparestheretrievalperformanceoftheone-step nosyntaxerrorswerefoundinoursamples,indi- |
| BM25retrieverusedinthebaselinestotheiterative catingthatCodexeffectivelygeneratesexecutable |
| step-by-stepBM25retrieverin PROGRAMFC. programsthroughfew-shotin-contextlearning. |
| 6987 |
| |
| Claim: |
| Emery, located in the same state as Edison Local School District, is a ghost town. It is near the |
| city that lies close to the Ohio Turnpike, a 241.26 mi highway. |
| Predicted Program: |
| answer_1 = Question("Which state is Emery located in?") |
| answer_2 = Question("Which state is Edison Local School District located in?") |
| fact_1 = Verify("{answer_1} and {answer_2} are the same state.") |
| fact_2 = Verify("Emery is a ghost town.") |
| answer_3 = Question("Which city is near Emery?") |
| answer_4 = Question("Which city lies close to the Ohio Turnpike, a 241.26 mi highway?") |
| fact_3 = Verify("{answer_3} is near {answer_4}.") fact_3 = Verify(“Emery is near {answer_4}.”) |
| label = Predict(fact_1 and fact_2 and fact_3) |
| Figure6: AnerrorcasefromtheHOVER4-hopdatasetwherethegeneratedreasoningprogramhasanincorrect |
| programstructure. Theincorrectsegment(s)aremarkedinred,andthecorrectrevisionsaremarkedingreen. |
| 1 |
| | | | | | Proportion(%) | | | | | HOVER | | | |
| | --- | --- | --- | --- | ------------- | --- | --- | --- | --- | ----- | --- | --- | |
| ErrorType |
| | | | | 2-hop | 3-hop | 4-hop | | Model | | | | FEVEROUS | |
| | --- | ------------- | --- | ----- | ----- | ----- | --- | ----------- | --- | ----------- | ----- | -------- | |
| | | | | | | | | | | 2-hop 3-hop | 4-hop | | |
| | | Syntaxerror | | 0% | 0% | 0% | | InstructGPT | | | | | |
| | | Semanticerror | | 29% | 38% | 77% | | | | | | | |
| | | | | | | | | - Direct | | 56.51 51.75 | 49.68 | 60.13 | |
| | | Token | | 8% | 20% | | 18% | | | | | | |
| | | | | | | | | - ZS-CoT | | 50.30 52.30 | 51.58 | 54.78 | |
| | | Structure | | 19% | 13% | | 57% | | | | | | |
| - CoT |
| | | | | | | | | | | 57.20 53.66 | 51.83 | 61.05 | |
| | --- | ------------------ | --- | --- | --- | --- | --- | ---------- | --- | ----------- | ----- | ----- | |
| | | Subtask | | 2% | 5% | | 2% | | | | | | |
| | | | | | | | | - Self-Ask | | 51.54 51.47 | 52.45 | 56.82 | |
| | | Incorrectexecution | | 71% | 62% | 23% | | | | | | | |
| Codex |
| | | | | | | | | | | 55.57 53.42 | 45.59 | 57.85 | |
| | --- | --- | --- | --- | --- | --- | --- | ------- | --- | ----------- | ----- | ----- | |
| | | | | | | | | FLAN-T5 | | 48.27 52.11 | 51.13 | 55.16 | |
| Table2: Reasoningprogramevaluationforincorrectly- |
| predictedexamplesfromeachhoplengthinHOVER. ProgramFC 54.27 59.66 |
| | | | | | | | | | | 54.18 | 52.88 | | |
| | --- | ------- | --------- | ------- | ------- | ---- | ------ | --------------------------- | --- | ----- | --------------------- | --- | |
| | | | | | | | | Table3: Closed-booksetting: | | | macro-F1scoresforPRO- | | |
| | | Second, | for 2-hop | claims, | we find | that | 71% of | | | | | | |
| GRAMFCandforthebaselines. |
| | the | programs | are | correct. | The majority | of | the er- | | | | | | |
| | --- | -------- | --- | -------- | ------------ | --- | ------- | --- | --- | --- | --- | --- | |
| rorsaretheresultofincorrectprogramexecution, |
| | | | | | | | | We also | include | the 175B-parameter | | Instruct- | |
| | --- | --- | --- | --- | --- | --- | --- | ------- | ------- | ------------------ | --- | --------- | |
| wherethequestionansweringorthefact-checking |
| modulesfailedtoreturnthecorrectanswer. GPT (text-davinci-002) (Ouyang et al., 2022) |
| Third,asthecomplexityoftheclaimsincreases, with four different prompts: (i) direct prompt- |
| | | | | | | | | ing with | the claim, | (ii) | CoT (Wei | et al., 2022) or | |
| | --- | --- | --- | --- | --- | --- | --- | -------- | ---------- | ---- | -------- | ---------------- | |
| theproportionofsemanticerrorsintheprograms |
| chain-of-thoughtpromptingwithdemonstrations, |
| alsoincreases,withstructuralerrorsbecomingpar- |
| ticularlyprevalent. Thishighlightsthedifficultyof (iii) ZS-CoT (Kojima et al., 2022) or zero-shot |
| generatingtheappropriatestep-by-stepreasoning chain-of-thoughtwiththeprompt“let’sthinkstep |
| | | | | | | | | by step”, | and (iv) | Self-Ask | (Press | et al., 2022), | |
| | ---------- | --- | ---------- | ---- | ------------------ | --- | ---- | --------- | -------- | -------- | ------ | -------------- | |
| | strategies | | for claims | that | require long-chain | | rea- | | | | | | |
| soning. An example structural error is shown in whichisavariantofCoTthatguidesthemodelrea- |
| Figure6,wherethemodelfailstoparsethesecond soningbyaskingaseriesofquestions. Thedetailed |
| promptingtemplatesaregiveninAppendixE. |
| sentenceoftheclaimintocorrectprograminstruc- |
| Ourresults,presentedinTable3,showthatmost |
| | tions. | Additional | | errorexamples | can | befound | in | | | | | | |
| | ---------- | ---------- | --- | ------------- | --- | ------- | --- | -------------- | -------- | ---------- | ------------ | ------------- | |
| | AppendixC. | | | | | | | models achieve | | a Macro-F1 | score | only slightly | |
| | | | | | | | | above random | guessing | | on the HOVER | dataset, | |
| 4.4 Closed-BookFact-Checking |
| indicatingthedifficultyofsolelyrelyingonpara- |
| Finally,weevaluatetheclosed-booksetting,where metric knowledge of large language models for |
| themodeldoesnothaveaccesstoanyknowledge fact-checkingcomplexclaims. Similartotheobser- |
| sourceandneedstorelyonitsparametricknowl- vationsinSection4.1,weseeatrendofimproved |
| edgeonly. ThebaselinemodelsfromgroupsIand performance as the number of the required rea- |
| II in Table 1 are trained with (evidence, claim) soninghopsincreases. Chain-of-thoughtprompt- |
| pairs and thus are not applicable in this setting. ingscoresanaverage2.7pointshigherthandirect |
| Wecompareourmethodtothebaselinesthatuse prompting, highlighting the importance of step- |
| largelanguagemodelsforin-contextlearning,in- by-step reasoning for complex fact-checking. It |
| cludingCodex(code-davinci-002)andFLAN-T5 outperformsourPROGRAMFConHOVER2-hop |
| | fromTable1. | | | | | | | andFEVEROUSbutperformsworseonHOVER | | | | | |
| | ----------- | --- | --- | --- | --- | --- | --- | ---------------------------------- | --- | --- | --- | --- | |
| 6988 |
| |
| 3-hopand4-hop. Generating reasoning programs for such implicit |
| ThiscanbeduetoCoTgeneratingfree-formex- complex claims requires a deeper understanding |
| planations,whichcanleadtounpredictableerrors of the claim and also access to world and com- |
| inlongreasoningchains. Incontrast,ourprogram monsenseknowledge. Weconductedpreliminary |
| generation-and-execution strategy is more stable experimentsonthesetypesofclaims,butwefound |
| forlongerreasoningchains. that our Codex-based generator struggled to pro- |
| | | | | | | | | duceacorrectreasoningprogram. | | | | Thishighlights | |
| | --- | --- | --- | --- | --- | --- | --- | ----------------------------- | --- | --- | --- | -------------- | |
| 5 ConclusionandFutureWork thegapinapplyingourPROGRAMFCtofact-check |
| | | | | | | | | real-worldclaims. | | Addressingthesechallengesis | | | |
| | ----------- | --- | ---------- | --- | ---------- | --- | ------ | ----------------- | --- | --------------------------- | --- | --- | |
| | We proposed | | PROGRAMFC, | | a few-shot | | neuro- | | | | | | |
| animportantdirectionforfuturework. |
| | symbolic | model | for | fact-checking | | that | learns to | | | | | | |
| | -------- | ----- | --- | ------------- | --- | ---- | --------- | ------- | --------- | --- | --------------------- | --- | |
| | | | | | | | | Second, | PROGRAMFC | | incursahighercomputa- | | |
| mapinputclaimstoareasoningprogramconsisting |
| tionalcostthanbaselineend-to-endfact-checking |
| ofasequenceofsub-taskfunctioncallsforanswer- |
| | | | | | | | | models. | Itrequirescallinglargelanguagemodels | | | | |
| | --------------- | --- | ----------------- | --- | --- | ------ | ------ | ------- | ------------------------------------ | --- | --- | --- | |
| | ing a question, | | for fact-checking | | a | simple | claim, | | | | | | |
| forprogramgenerationandfurthercallingmultiple |
| | andforcomputingalogicalexpression. | | | | | Thenfact- | | | | | | | |
| | ---------------------------------- | --- | --- | --- | --- | --------- | --- | --------------- | --- | ---------------------------- | --- | --- | |
| | | | | | | | | sub-taskmodels. | | Thisresultsintheactualcompu- | | | |
| checkingisperformedbyexecutingthatprogram. |
| | | | | | | | | tationaltimethatis | | | 4–5 higherthanforanend- | | |
| | --------------- | --- | -------- | ------------------ | ---------- | --- | -------- | ------------------- | --- | --- | ----------------------- | --- | |
| | PROGRAMFC | | combines | the | advantages | | of sym- | | | ∼ | × | | |
| | | | | | | | | to-endFLAN-T5model. | | | Developingmoreefficient | | |
| | bolic programs, | | such | as explainability, | | | with the | | | | | | |
| methodsforprogramgenerationandexecutionis |
| | flexibility | of | end-to-end | neural | models. | | Using | | | | | | |
| | ----------- | --- | ---------- | ------ | ------- | --- | ----- | --- | --- | --- | --- | --- | |
| animportantdirectionforfuturework. |
| | Codex as | the | program | generator, | | PROGRAMFC | | | | | | | |
| | --------------------------------------- | --- | ------- | ---------- | --- | --------- | --- | --------------- | --- | --- | --- | --- | |
| | demonstratespromisingperformanceonHOVER | | | | | | | EthicsStatement | | | | | |
| andFEVEROUSwithonlyasmallnumberofin- |
| contextdemonstrationsandnoadditionaltraining. Biases. Wenotethattheremightbesomebiases |
| | | | | | | | | in the data | used | to train | the LLMs, | as well as in | |
| | --- | --- | --- | --- | --- | --- | --- | ----------- | ---- | -------- | --------- | ------------- | |
| Wealsoinvestigatedtheimpactofmodelsizeand |
| | | | | | | | | factualityjudgments. | | | Botharebeyondourcontrol. | | |
| | ---------------- | --- | ----------------------- | --- | ---------- | --- | ------ | -------------------- | --- | --- | ------------------------ | --- | |
| | the benefits | of | programs | for | retrieval, | and | we an- | | | | | | |
| | alyzedtheerrors. | | Theresultsindicatedthat | | | | PRO- | | | | | | |
| Ourmod- |
| IntendedUseandMisusePotential. |
| | GRAMFC | effectively | | balances | model | capability, | | | | | | | |
| | ------ | ----------- | --- | -------- | ----- | ----------- | --- | ------- | -------------- | --- | -------------- | ---------- | |
| | | | | | | | | els can | be of interest | | to the general | public and | |
| learningefficiency,andinterpretability. |
| couldalsosavealotoftimetohumanfact-checkers. |
| Infuturework,wewanttoadaptPROGRAMFC |
| However,theycouldalsobemisusedbymalicious |
| tomorereal-worldfact-checkingscenarios,suchas |
| | | | | | | | | actors. | Weaskresearcherstoexercisecaution. | | | | |
| | --- | --- | --- | --- | --- | --- | --- | ------- | ---------------------------------- | --- | --- | --- | |
| fakenewsdetectionandmulti-modalfact-checking, |
| | | | | | | | | | | | The | use of large lan- | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | ----------------- | |
| withadvancedreasoningprogramdesignandsub- Environmental Impact. |
| taskfunctionalities. guage models requires a significant amount of |
| | | | | | | | | energy | for computation | | for training, | which con- | |
| | --- | --- | --- | --- | --- | --- | --- | ------ | --------------- | --- | ------------- | ---------- | |
| Limitations |
| | | | | | | | | tributestoglobalwarming. | | | Ourworkperformsfew- | | |
| | --- | --- | --- | --- | --- | --- | --- | ------------------------ | --- | --- | ------------------- | --- | |
| shotin-contextlearninginsteadoftrainingmodels |
| | Weidentifytwomainlimitationsof | | | | | PROGRAMFC. | | | | | | | |
| | ------------------------------ | --- | --- | --- | --- | ---------- | --- | --- | --- | --- | --- | --- | |
| fromscratch,sotheenergyfootprintofourworkis |
| First,despitebeingcomplexintheirsurfaceform, |
| | | | | | | | | less. Thelargelanguagemodel(Codex)whoseAPI | | | | | |
| | --- | --- | --- | --- | --- | --- | --- | ------------------------------------------ | --- | --- | --- | --- | |
| theclaimsintheHOVERandFEVEROUSdatasets |
| weuseforinferenceconsumessignificantenergy. |
| | mostlyrequireonlyexplicit | | | | multi-stepreasoning, | | | | | | | | |
| | ------------------------- | --- | --- | --- | -------------------- | --- | --- | --- | --- | --- | --- | --- | |
| i.e., the decomposition can be derived from the Acknowledgements |
| | claim’s | syntactic | structure | | or how | the | claim is | | | | | | |
| | ------- | --------- | --------- | --- | ------ | --- | -------- | --- | --- | --- | --- | --- | |
| framed. Thislowersthedifficultyofgeneratingrea- This work was supported in part by the National |
| ScienceFoundationaward#2048122andbySin- |
| | soning programs. | | However, | | for many | real-world | | | | | | | |
| | ---------------- | --- | -------- | --- | -------- | ---------- | --- | --- | --- | --- | --- | --- | |
| gapore’sMinistryofEducationTier3grant“Dig- |
| | complex | claims, | the | reasoning | is | often | implicit. | | | | | | |
| | ------- | ------- | --- | --------- | --- | ----- | --------- | --- | --- | --- | --- | --- | |
| Forexample,fortheclaim“Aristotlecouldn’thave ital Information Resilience: Restoring Trust and |
| | | | | | | | | NudgingBehavioursinDigitalisation”. | | | | Theviews | |
| | --- | --- | --- | --- | --- | --- | --- | ----------------------------------- | --- | --- | --- | -------- | |
| usedalaptop”,thereasoningprogramis: |
| expressedarethoseoftheauthorsanddonotreflect |
| answer_1=Question(“WhendidAristotlelive?”); |
| theofficialpolicyorpositionoftheUSgovernment. |
| | answer_2 | = Question(“When | | | was | the laptop | in- | | | | | | |
| | -------- | ---------------- | --- | --- | --- | ---------- | --- | --- | --- | --- | --- | --- | |
| WethankAlexMei,XinyiWang,DanqingWang, |
| vented?”); |
| SharonLevy,GyuwanKim,andothermembersof |
| fact_1=Verify(“answer_1isbeforeanswer_2.”); |
| theUCSBNLPgroupfortheirvaluablefeedback. |
| label=Predict(fact_1) |
| 6989 |
| |
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| | | | | | | | soning for | fact verification. | In Proceedings | | of the | |
| | ------------- | ---- | -------- | ----- | ----- | ------ | ---------- | ------------------ | -------------- | --- | ------ | |
| | David Wadden, | Kyle | Lo, Lucy | Wang, | Arman | Cohan, | | | | | | |
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| | Iz Beltagy, | and Hannaneh | | Hajishirzi. | 2022b. | Mul- | | | | | | |
| | ----------- | ------------ | --- | ----------- | ------ | ---- | --- | --- | --- | --- | --- | |
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| tiVerS:Improvingscientificclaimverificationwith |
| Italy. |
| | weaksupervisionandfull-documentcontext. | | | | | InFind- | | | | | | |
| | --------------------------------------- | --- | --- | --- | --- | ------- | --- | --- | --- | --- | --- | |
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| tics:NAACL2022,pages61–76,Seattle,Washington, |
| USA. |
| 6993 |
| |
| A ImplementationDetailsaboutthe In this category, we selected the following three |
| | Baselines | | | | | | fine-tunedmodels: | | | | | | | | |
| | --------- | --- | --- | --- | --- | --- | ----------------- | --- | --- | --- | --- | --- | --- | --- | |
| Inthissection,wegivetheimplementationdetails • RoBERTa-NLI (Nie et al., 2020) fine-tunes |
| | forthesevenbaselinesweusedinourwork. | | | | | | Typ- | | | | | | | | |
| | ------------------------------------ | --- | --- | --- | --- | --- | ---- | ------------- | --- | ---- | --- | ---------- | --- | ------ | |
| | | | | | | | | RoBERTa-large | | (Liu | et | al., 2019) | on | a com- | |
| ical ways to perform few-shot fact-checking us- bination of four well-known NLI datasets: |
| inglargelanguagemodelsarefine-tuningandin- SNLI(Bowmanetal.,2015),MNLI(Williams |
| | contextlearning. | | Thus,wecategorizethebaselines | | | | | | | | | | | | |
| | ---------------- | --- | ----------------------------- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| etal.,2018),FEVER-NLI(Nieetal.,2019), |
| | intothreecategories. | | | | | | | ANLI(R1,R2,R3)(Nieetal.,2020). | | | | | | Weused | |
| | -------------------- | --- | --- | --- | --- | --- | --- | ------------------------------ | --- | --- | --- | --- | --- | ------ | |
| thepublicmodelcheckpointavailableatHug- |
| A.1 Pre-trainedModels |
| | | | | | | | | gingFace4 | | and we | further | fine-tuned | | it with | |
| | ----------- | ------ | --- | ---------- | --- | ---------- | --- | --------- | --- | ------ | ------- | ---------- | --- | ------- | |
| | Pre-trained | models | use | pretrained | | Transform- | | | | | | | | | |
| 20randomexamplesfromHOVER/FEVER- |
| | ers (Vaswani | et | al., 2017) | such | as | BERT | (Devlin | OUS. | | | | | | | |
| | ------------- | --- | ---------- | ---- | ---- | ----- | --------- | ---- | --- | --- | --- | --- | --- | --- | |
| | et al., 2019) | and | T5 (Raffel | et | al., | 2020) | for fact- | | | | | | | | |
| DeBERTaV3-NLI |
| checking. Forfew-shotlearning,wefine-tunethem • (He et al., 2021) fine- |
| | | | | | | | | tunes | the | DeBERTaV3-large | | | model | on | |
| | --- | --- | --- | --- | --- | --- | --- | ----- | --- | --------------- | --- | --- | ----- | --- | |
| using20randomlysampledtrainingexamplesfrom |
| 885,242NLIhypothesis–premisepairsfrom |
| | HOVER | or FEVEROUS. | | We | ran the | training | 10 | | | | | | | | |
| | ----- | ------------ | --- | --- | ------- | -------- | --- | --- | --- | --- | --- | --- | --- | --- | |
| timeswithdifferentrandomseedsandreporttheav- FEVER and on four NLI datasets: MNLI, |
| erageperformanceonthevalidationset. Wechose ANLI, LingNLI (Parrish et al., 2021), and |
| | | | | | | | | WANLI | (Liu | et al., | 2022). | This | is | the best- | |
| | --- | --- | --- | --- | --- | --- | --- | ----- | ---- | ------- | ------ | ---- | --- | --------- | |
| twomodels: |
| performingNLImodelonHuggingFaceasof |
| | • BERT-FC | | (Soleimani | et | al., 2020): | | It uses | | | | | | | | |
| | --------- | --- | ---------- | --- | ----------- | --- | ------- | --- | --- | --- | --- | --- | --- | --- | |
| 06/06/2022.5 |
| | BERT | for | claim | verification. | | The | claim | | | | | | | | |
| | ---- | --- | ----- | ------------- | --- | --- | ----- | --- | --- | --- | --- | --- | --- | --- | |
| and the evidence are concatenated ([CLS] • MULTIVERS(Waddenetal.,2022b),formerly |
| | claim | [SEP] | evidence) | | | | | | | | | | | | |
| | ----- | ----- | --------- | --- | --- | ---- | ------ | ----- | --- | ------------ | --- | ---- | --- | ----- | |
| | | | | | and | used | as in- | known | as | LongChecker, | | uses | the | Long- | |
| put for a binary classification task to pre- Former (Beltagy et al., 2020) for claim ver- |
| dict the veracity label of the claim. We use ification to address the long input evidence |
| thebert-large-uncased(345Mparameters) |
| | | | | | | | | problem. | | We use | a model | checkpoint | | fine- | |
| | ---------------------------- | --- | --- | --- | --- | --- | --- | -------------- | --- | ------ | ------- | ---------- | --- | ----- | |
| | modelprovidedinHuggingFace.2 | | | | | | | tunedonFEVER.6 | | | | | | | |
| LisT5 |
| • (Jiang et al., 2021): This is a fact- A.3 In-ContextLearningModels |
| | checking | | framework | built | with | a pretrained | | | | | | | | | |
| | -------- | --- | --------- | ----- | ---- | ------------ | --- | --- | --- | --- | --- | --- | --- | --- | |
| Thesemodelshaverecentlyshownstrongfew-shot |
| | sequence-to-sequence | | | transformer, | | | namely | | | | | | | | |
| | ----------------------------------- | --- | --- | ------------ | --- | --- | --------------------------------- | --- | --- | --- | --- | --- | --------- | --- | |
| | | | | | | | learningabilityinvariousNLPtasks. | | | | | | Byprompt- | | |
| | T5(Raffeletal.,2020),asitsbackbone. | | | | | | We | | | | | | | | |
| ingalargelanguagemodelwithafewin-context |
| adoptthe“listwiseconcatenation”proposedin |
| examples,themodelcanquicklylearnataskfrom |
| thepaperforlabelprediction,whichconcate- |
| | | | | | | | demonstrations. | | | Tomakeafaircomparisontoour | | | | | |
| | --- | --- | --- | --- | --- | --- | --------------- | --- | --- | -------------------------- | --- | --- | --- | --- | |
| natesallcandidateevidencesentencesintoa |
| model,wechoosetwoin-contextlearningbaselines |
| singleinputandwetrainthet5-largemodel |
| asfollows. |
| todirectlyclassifytheclaimasSupportedor |
| Refuted. Weusetheoriginalimplementation • Codex (Chen et al., 2021) is used in |
| ofthismodel.3 |
| | | | | | | | | our | model | to generate | | reasoning | programs. | | |
| | --- | --- | --- | --- | --- | --- | --- | --- | --------------- | ----------- | -------- | --------- | --------- | ---- | |
| | | | | | | | | One | straightforward | | baseline | | directly | uses | |
| A.2 FC/NLIFine-TunedModels |
| | | | | | | | | it for | fact-checking. | | | To | this | end, we | |
| | --- | --- | --- | --- | --- | --- | --- | ------ | -------------- | --- | --- | --- | ---- | ------- | |
| ThesemodelsarepretrainedTransformermodels |
| | | | | | | | | prompt | Codex | (code-davinci-002) | | | | as fol- | |
| | ----------------- | -------- | ------------ | ---------- | --------- | ---- | -------- | ------------ | ----------- | ------------------ | ---------- | -------- | ------------- | ------- | |
| | that have | been | specifically | fine-tuned | | on | single- | | | | | | | | |
| | | | | | | | | lows: | “<Evidence> | | Based | | on the | above | |
| | hop fact-checking | | datasets | (e.g., | FEVER) | | or nat- | | | | | | | | |
| | | | | | | | | information, | | is | it | true | that <Claim>? | | |
| | ural language | | inference | (NLI) | datasets. | This | ad- | | | | | | | | |
| | | | | | | | | True | or False? | | The answer | | is:”. | Wepre- | |
| | ditional | training | allows | these | models | to | excel at | | | | | | | | |
| | | | | | | | | fix the | same | 20 in-context | | examples | | for our | |
| fact-checkingsimpleclaims,andthustheycangen- |
| modelbeforethepromptasdemonstrations. |
| eralizebettertocomplexclaimsthatrequiremulti- |
| 4https://huggingface.co/ynie/ |
| hopreasoningduringfurtherfew-shotfine-tuning. |
| roberta-large-snli_mnli_fever_anli_R1_R2_R3-nli |
| 2https://huggingface.co/ 5https://huggingface.co/MoritzLaurer/ |
| 3https://github.com/castorini/pygaggle/tree/ DeBERTa-v3-large-mnli-fever-anli-ling-wanli |
| master/experiments/list5 6https://github.com/dwadden/multivers |
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| |
| • FLAN-T5(Chungetal.,2022)isanimproved D ProgramGenerationPrompts |
| | version | of | T5, which | is fine-tuned | | on 1.8K | | | | | | |
| | ------- | --- | --------- | ------------- | --- | ------- | --- | --- | --- | --- | --- | |
| OurmanuallywrittenpromptsfortheHOVERand |
| tasksphrasedasinstructions,withandwithout |
| theFEVEROUS-SdatasetsaregiveninListings1 |
| | exemplars,i.e.,zero-shotandfew-shot. | | | | | The | | | | | | |
| | ------------------------------------ | --- | --- | --- | --- | --- | --- | --- | --- | --- | --- | |
| and2,respectively. |
| modelhasshownstrongperformanceinvar- |
| iousin-contextfew-shotlearningNLPtasks, |
| E PromptsforClosed-Book |
| | such | as reasoning, | | and question-answering. | | | | | | | | |
| | ---- | ------------- | --- | ----------------------- | --- | --- | --- | --- | --- | --- | --- | |
| Fact-Checking |
| | We | prompt | the model | with the | same | format | | | | | | |
| | --- | ------ | --------- | -------- | ---- | ------ | --- | --- | --- | --- | --- | |
| as we used in Section 3.4: “<Evidence> Q: Belowweshowthetemplatesforthefourprompt- |
| <Claim> Is it true that <Claim>? True ing methods used for InstructGPT for the closed- |
| or False? The answer is:”,prefixingwith bookfact-checkingsettinginSection4.4. |
| | thesame20in-contextexamples. | | | | Wealsouse | | DirectPrompting | | | | | |
| | ---------------------------- | --- | --- | --- | --------- | --- | --------------- | --- | --- | --- | --- | |
| thesamemodelsize(FLAN-T5-XXL3B)with |
| | | | | | | | # Answer | the following | true/false | questions: | | |
| | --- | --- | --- | --- | --- | --- | -------- | ------------- | ---------- | ---------- | --- | |
| ourmodelforfaircomparison. |
| | | | | | | | Is it true | that The | woman | the story behind | Girl Crazy | |
| | ------------------------------ | --- | --- | --- | --- | --- | ----------- | ------------------- | ----- | ---------------- | ---------- | |
| | | | | | | | is credited | to is older | than | Ted Kotcheff? | | |
| | B ExamplesofGeneratedReasoning | | | | | | The answer | is: False | | | | |
| | Programs | | | | | | ( more | in-context examples | here | ) | | |
| | | | | | | | ··· | | | ··· | | |
| | | | | | | | Is it true | that <input_claim>? | | | | |
| Figure7showssixexamplesofgeneratedreason- |
| | | | | | | | The answer | is: | | | | |
| | ---------------- | --- | --------- | ---------------- | --- | --- | --------------- | ------------- | ---------- | --------- | --- | |
| | ingprogramsby | | PROGRAMFC | thatcoverdiverse | | | | | | | | |
| | reasoningchains. | | | | | | ZS-CoTPrompting | | | | | |
| | | | | | | | # Answer | the following | true/false | question: | | |
| C ErrorAnalysisforReasoning |
| | | | | | | | Is it true | that <input_claim>? | | True or False? | | |
| | -------- | ----- | ------------- | --- | --------- | ----- | ------------ | ------------------- | --- | -------------- | --- | |
| | Programs | | | | | | Let us think | step-by-step. | | The answer is: | | |
| | Figure 8 | shows | five examples | of | erroneous | cases | CoTPrompting | | | | | |
| wherethegeneratedreasoningprogramsareincor- |
| | | | | | | | # Answer | the following | true/false | questions: | | |
| | --- | --- | --- | --- | --- | --- | -------- | ------------- | ---------- | ---------- | --- | |
| rect. Weprovideexplanationsforeachoftheerror |
| | | | | | | | Is it true | that The | woman | the story behind | Girl Crazy | |
| | --- | --- | --- | --- | --- | --- | ----------- | ----------- | ----- | ---------------- | ---------- | |
| | | | | | | | is credited | to is older | than | Ted Kotcheff? | | |
| casesbelow: |
| | | | | | | | Let's think | step by | step. | | | |
| | --- | --- | --- | --- | --- | --- | ------------ | -------- | -------- | ---------- | --------- | |
| | | | | | | | Girl Crazy's | story is | credited | to Hampton | Del Ruth. | |
| Example1 Itgeneratesawronglogicalreason- Hampton Del Ruth was born on September 7, 1879. |
| | | | | | | | Ted Kotcheff | was born | on April | 7, 1931. | | |
| | --- | --- | --- | --- | --- | --- | ------------ | -------- | -------- | -------- | --- | |
| ing operator for the final step. The correct logic Therefore, the answer is: False. |
| | should be | “not | (fact_1 | and fact_2)” | | instead | | | | | | |
| | --------- | ---- | -------- | ------------ | --- | ------- | ----------- | ------------------- | ----- | --- | --- | |
| | | | | | | | ( more | in-context examples | here | ) | | |
| | of“fact_1 | and | fact_2”. | | | | ··· | | | ··· | | |
| | | | | | | | Is it true | that <input_claim>? | | | | |
| | | | | | | | Let's think | step by | step. | | | |
| Itfailstoperformco-referencereso- |
| Example2 |
| lutionfortheargumentsinthethirdandthefourth |
| Self-AskPrompting |
| | reasoningsteps. | | “Thisalbum”shouldbereplaced | | | | | | | | | |
| | --------------- | --- | --------------------------- | --- | --- | --- | -------- | ------------- | ---------- | ---------- | --- | |
| | | | | | | | # Answer | the following | true/false | questions: | | |
| with“Thebluegrass”tomakethesub-taskcontext- |
| | | | | | | | Is it true | that The | woman | the story behind | Girl Crazy | |
| | ------------ | ----- | -------- | ------ | --- | -------- | ------------ | ----------- | ---------- | ---------------- | ---------- | |
| | independent. | “This | musical” | should | be | replaced | | | | | | |
| | | | | | | | is credited | to is older | than | Ted Kotcheff? | | |
| | | | | | | | Q: The story | behind | Girl Crazy | is credited | to whom? | |
| withthevariable“answer_1”fromthefirststep. |
| | | | | | | | A: Hampton | Del Ruth | | | | |
| | -------------------------------------------- | ---------------------------------- | ------- | ----------------- | --------- | --------- | ------------- | ------------------- | ---------- | ------------------ | --- | |
| | | | | | | | Q: Is Hampton | Del Ruth | older | than Ted Kotcheff? | | |
| | Example3 | Itfailstocreateameaningfulprob- | | | | | A: No | | | | | |
| | | | | | | | So the final | answer | is: False. | | | |
| | lemdecompositionfortheclaim. | | | Itgeneratesatriv- | | | | | | | | |
| | | | | | | | ( more | in-context examples | here | ) | | |
| | ialprogramthatsimplyrepeatstheoriginalclaim. | | | | | | ··· | | | ··· | | |
| | | | | | | | Is it true | that <input_claim>? | | | | |
| | Example4 | Itfailstogenerateafine-grainedrea- | | | | | | | | | | |
| | soning structure | | for the | input claim. | It | also gen- | | | | | | |
| | erates a | trivial program | | that simply | separates | the | | | | | | |
| claimintosentences. |
| | Example | 5 It | generates | a redundant | | reason- | | | | | | |
| | --------- | --------------- | --------- | ----------- | ------------ | -------- | --- | --- | --- | --- | --- | |
| | ing step | “Question("When | | was | the musician | | | | | | | |
| | born?")”, | which | does not | add any | new | informa- | | | | | | |
| tiontothereasoningchain. |
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|
| Claim: |
| Tritonia and Phyteuma are both names for a plant genus. |
| Predicted Program: |
| fact_1 = Verify("Tritonia is a name for a plant genus.") |
| fact_2 = Verify("Phyteuma is a name for a plant genus.") |
| label = Predict(fact_1 and fact_2) |
| Claim: |
| The country that Fujairah College is located in had a 2013 population of 9.2 million until it was |
| hit by the plague in 1483 when the population was halved. |
| Predicted Program: |
| answer_1 = Question("Which country is Fujairah College located in?") |
| fact_1 = Verify("{answer_1} had a 2013 population of 9.2 million.") |
| fact_2 = Verify("{answer_1} was hit by the plague in 1483.") |
| fact_3 = Verify("The population of {answer_1} was halved in 1483.") |
| label = Predict(fact_1 and fact_2 and fact_3) |
| Claim: |
| The first female board member for the Liberal Party, she was born in Vestfold county in Norway. |
| Predicted Program: |
| answer_1 = Question("Who was the first female board member for the Liberal Party?") |
| fact_1 = Verify("{answer_1} was born in Vestfold county in Norway.") |
| label = Predict(fact_1) |
| Claim: |
| The solicitor who won the show Back to Reality ahead of Maureen Rees and Craig Phillips is |
| English. The solicitor that was a chair of Global Witness is also English. |
| Predicted Program: |
| answer_1 = Question("Which solicitor won the show Back to Reality ahead of Maureen Rees and Craig |
| Phillips?") |
| answer_2 = Question("Which solicitor was a chair of Global Witness?") |
| fact_1 = Verify("{answer_1} is English.") |
| fact_2 = Verify("{answer_2} is English.") |
| label = Predict(fact_1 and fact_2) |
| Claim: |
| Anthony Burgess addressed the novelist and essayist, the author of Grimus, in a lengthy love |
| letter. The author is of the same nationality as Raj Koothrappali. |
| Predicted Program: |
| answer_1 = Question("Which novelist and essayist did Anthony Burgess address in a lengthy love |
| letter?") |
| fact_1 = Verify("{answer_1} is the author of Grimus.") |
| answer_2 = Question("What is the nationality of Raj Koothrappali?") |
| fact_2 = Verify("{answer_1} is of the same nationality as {answer_2}.") |
| label = Predict(fact_1 and fact_2) |
| Claim: |
| The critically acclaimed film, that Buddy Baker scored in 1975, is a Walt Disney film. It was |
| produced first before the film that featured Bruce M. Fischer as Mr. Coogar. |
| Predicted Program: |
| answer_1 = Question("Which critically acclaimed film did Buddy Baker score in 1975?") |
| fact_1 = Verify("{answer_1} is a Walt Disney film.") |
| answer_2 = Question("Which film featured Bruce M. Fischer as Mr. Coogar?") |
| fact_2 = Verify("{answer_1} was produced first before {answer_2}.") |
| label = Predict(fact_1 and fact_2) |
| Figure7: ExamplesofgeneratedreasoningprogramsbyPROGRAMFC. |
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|
| Semantic Error —Token: incorrect or missing arguments/variables |
| Example 1: |
| Bitter Jester and The Future of Food are not both documentaries about food. |
| Predicted Program: |
| fact_1 = Verify("Bitter Jester is a documentary about food.") |
| fact_2 = Verify("The Future of Food is a documentary about food.") |
| label = Predict(fact_1 and fact_2) label = Predict(not (fact_1 and fact_2)) |
| Example 2: |
| The record producer that produced the bluegrass album was born on 22 June, 1944. This album |
| inspired a Tony award winning musical. This musical had a character that was originated by Carmen |
| Cusack. |
| Predicted Program: |
| answer_1 = Question("Which record producer produced the bluegrass album?") |
| fact_1 = Verify("{answer_1} was born on 22 June, 1944.") |
| fact_2 = Verify("This album inspired a Tony award winning musical.") |
| fact_3 = Verify("This musical had a character that was originated by Carmen Cusack.") |
| label = Predict(fact_1 and fact_2 and fact_3) |
| answer_1 = Question("Which record producer produced the bluegrass album?") |
| fact_1 = Verify("{answer_1} was born on 22 June, 1944.") |
| fact_2 = Verify("The bluegrass album inspired a Tony award winning musical.") |
| fact_3 = Verify("{answer_1} had a character that was originated by Carmen Cusack.") |
| label = Predict(fact_1 and fact_2 and fact_3) |
| Semantic Error —Structure: incorrect program structure |
| Example 3: |
| Richard Strauss, German composer of Die Nacht, composed another well-established opera called Der |
| Rosenkavalier. |
| Predicted Program: |
| fact_1 = Verify("Richard Strauss, German composer of Die Nacht, composed another well-established |
| opera called Der Rosenkavalier.") |
| label = Predict(fact_1) |
| fact_1 = Verify("Richard Strauss is a German composer of Die Nacht.”) |
| fact_2 = Verify("Richard Strauss composed a well-established opera called Der Rosenkavalier.") |
| label = Predict(fact_1 and fact_2) |
| Example 4: |
| The film Deanna Oliver produced in 1999 grossed $36.8 million domestically. The musical film |
| based on coach Herman Boone, did not. |
| Predicted Program: |
| fact_1 = Verify("The film Deanna Oliver produced in 1999 grossed $36.8 million domestically.") |
| fact_2 = Verify("The musical film based on coach Herman Boone, did not.") |
| label = Predict(fact_1 and fact_2) |
| answer_1 = Question("What film did Deanna Oliver produce in 1999?") |
| fact_1 = Verify("{answer_1} grossed $36.8 million domestically.") |
| answer_2 = Question("What musical film was based on coach Herman Boone?") |
| fact_2 = Verify("{answer_2} grossed less than $36.8 million domestically.") |
| label = Predict(fact_1 and fact_2) |
| Semantic Error —Subtask: missing / redundant / incorrect sub-task calls |
| Example 5: |
| The musician, who founded Morningwood with Max Green, is older than Max Green. |
| Predicted Program: |
| answer_1 = Question("Who founded Morningwood with Max Green?") |
| answer_2 = Question("When was Max Green born?") |
| answer_3 = Question("When was the musician born?") |
| fact_1 = Verify("{answer_3} is older than {answer_2}.") {answer_1} is older than {answer_2}. |
| label = Verify(fact_1) |
| Figure8: Exampleerrorcaseswherethegeneratedreasoningprogramsareincorrect. Theincorrectsegment(s)are |
| markedinred,andthecorrectrevisionsaremarkedingreen. |
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| '''Generate a python-like program that describes the reasoning steps required to |
| verify the claim step-by-step. You can call three functions in the program: 1. |
| Question () to answer a question; 2. Verify () to verify a simple claim; 3. |
| | Predict() | to | predict | the | veracity | | label.''' | | | |
| | --------- | --- | ------- | --- | -------- | --- | --------- | --- | --- | |
| # The claim is that Howard University Hospital and Providence Hospital are both |
| | located | in Washington, | | | D.C. | | | | | |
| | ------- | -------------- | --- | --- | ---- | --- | --- | --- | --- | |
| def program(): |
| fact_1 = Verify("Howard University Hospital is located in Washington, D.C.") |
| fact_2 = Verify("Providence Hospital is located in Washington, D.C.") |
| | label = | Predict(fact_1 | | | and | fact_2) | | | | |
| | ------- | -------------- | --- | --- | --- | ------- | --- | --- | --- | |
| # The claim is that WWE Super Tuesday took place at an arena that currently goes by |
| | the name | TD Garden. | | | | | | | | |
| | -------- | ---------- | --- | --- | --- | --- | --- | --- | --- | |
| def program(): |
| answer_1 = Question("Which arena the WWE Super Tuesday took place?") |
| fact_1 = Verify(f"{answer_1} currently goes by the name TD Garden.") |
| | label = | Predict(fact_1) | | | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | --- | --- | |
| # The claim is that Talking Heads, an American rock band that was "one of the most |
| critically acclaimed bands of the 80's" is featured in KSPN's AAA format. |
| def program(): |
| fact_1 = Verify("Talking Heads is an American rock band that was 'one of the |
| | most critically | | acclaimed | | bands | of | the | 80's'.") | | |
| | --------------- | --- | --------- | --- | ----- | --- | --- | -------- | --- | |
| fact_2 = Verify("Talking Heads is featured in KSPN's AAA format.") |
| | label = | Predict(fact_1 | | | and | fact_2) | | | | |
| | ------- | -------------- | --- | --- | --- | ------- | --- | --- | --- | |
| # The claim is that An IndyCar race driver drove a Formula 1 car designed by Peter |
| | McCool | during | the | 2007 | Formula | One | season. | | | |
| | ------ | ------ | --- | ---- | ------- | --- | ------- | --- | --- | |
| def program(): |
| answer_1 = Question("Which Formula 1 car was designed by Peter McCool during the |
| | 2007 Formula | | One | season?") | | | | | | |
| | ------------ | --- | --- | --------- | --- | --- | --- | --- | --- | |
| fact_1 = Verify(f"An IndyCar race driver drove the car {answer_1}.") |
| | label = | Predict(fact_1) | | | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | --- | --- | |
| # The claim is that Gina Bramhill was born in a village. The 2011 population of the |
| | area that | includes | | this | village | was | 167,446. | | | |
| | --------- | -------- | --- | ---- | ------- | --- | -------- | --- | --- | |
| def program(): |
| answer_1 = Question("Which village was Gina Bramhill born in?") |
| fact_1 = Verify(f"The 2011 population of the area that includes {answer_1} was |
| 167,446.") |
| | label = | Predict(fact_1) | | | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | --- | --- | |
| # The claim is that Don Ashley Turlington graduated from Saint Joseph's College, a |
| | private | Catholic | liberal | | arts | college | | in Standish. | | |
| | ------- | -------- | ------- | --- | ---- | ------- | --- | ------------ | --- | |
| def program(): |
| fact_1 = Verify("Saint Joseph's College is a private Catholic liberal arts |
| | college | is located | | in Standish.") | | | | | | |
| | ------- | ---------- | --- | -------------- | --- | --- | --- | --- | --- | |
| fact_2 = Verify(f"Don Ashley Turlington graduated from Saint Joseph's College.") |
| | label = | Predict(fact_1 | | | and | fact_2) | | | | |
| | ------- | -------------- | --- | --- | --- | ------- | --- | --- | --- | |
| # The claim is that Gael and Fitness are not published in the same country. |
| def program(): |
| | answer_1 | = Question("Which | | | | country | was | Gael published | in?") | |
| | -------- | ----------------- | --- | --- | --- | ------- | --- | -------------- | ----- | |
| answer_2 = Question("Which country was Fitness published in?") |
| fact_1 = Verify(f"{answer_1} and {answer_2} are not the same country.") |
| | label = | Predict(fact_1) | | | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | --- | --- | |
| # The claim is that Blackstar is the name of the album released by David Bowie that |
| | was recorded | | in secret. | | | | | | | |
| | ------------ | --- | ---------- | --- | --- | --- | --- | --- | --- | |
| def program(): |
| fact_1 = Verify("David Bowie released an album called Blackstar.") |
| | fact_2 | = Verify("David | | | Bowie | recorded | | an album | in secret.") | |
| | ------- | --------------- | --- | --- | ----- | -------- | --- | -------- | ------------ | |
| | label = | Predict(fact_1 | | | and | fact_2) | | | | |
| # The claim is that In the 2004 Hockey film produced by a former major league |
| | baseball | pitcher | Kurt | Russell | | played | the | USA coach. | | |
| | -------- | ------- | ---- | ------- | --- | ------ | --- | ---------- | --- | |
| def program(): |
| answer_1 = Question("Which 2004 Hockey film was produced a former major league |
| 6998 |
| |
| | baseball | pitcher?") | | | | | | |
| | -------- | ---------- | --- | --- | --- | --- | --- | |
| fact_1 = Verify("Kurt Russell played the USA coach in the film {answer_1}.") |
| | label = | Predict(fact_1) | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | |
| # The claim is that Along with the New York Islanders and the New York Rangers, the |
| New Jersey Devils NFL franchise is popular in the New York metropolitan area. |
| def program(): |
| fact_1 = Verify("The New York Islanders and the New York Rangers are popular in |
| | the New | York metropolitan | | area.") | | | | |
| | ------- | ----------------- | --- | ------- | --- | --- | --- | |
| fact_2 = Verify("The New Jersey Devils NFL franchise is popular in the New York |
| | metropolitan | area.") | | | | | | |
| | ------------ | -------------- | --- | --- | ------- | --- | --- | |
| | label = | Predict(fact_1 | | and | fact_2) | | | |
| # The claim is that Jack McFarland is the best known role of the host of the 64th |
| | Annual Tony | Awards. | | | | | | |
| | ----------- | ------- | --- | --- | --- | --- | --- | |
| def program(): |
| answer_1 = Question("Who is the host of the 64th Annual Tony Awards?") |
| fact_1 = Verify(f\"Jack McFarland is the best known role of {answer_1}.") |
| | label = | Predict(fact_1) | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | |
| # The claim is that The song recorded by Fergie that was produced by Polow da Don |
| | and was | followed | by | Life Goes | On | was M.I.L.F.$. | | |
| | ------- | -------- | --- | --------- | --- | -------------- | --- | |
| def program(): |
| fact_1 = Verify("M.I.L.F.$ was recorded by Fergie that was produced by Polow da |
| Don.") |
| fact_2 = Verify("M.I.L.F.$ was was followed by Life Goes On.") |
| | label = | Predict(fact_1 | | and | fact_2) | | | |
| | ------- | -------------- | --- | --- | ------- | --- | --- | |
| # The claim is that Eatza Pizza and Your Pie were not founded in the same state. |
| def program(): |
| answer_1 = Question("Which state was Eatza Pizza founded in?") |
| | answer_2 | = Question("Which | | | state | was Your Pie founded | in?") | |
| | -------- | ----------------- | --- | --- | ----- | -------------------- | ----- | |
| fact_1 = Verify(f"{answer_1} and {answer_2} are not the same state.") |
| | label = | Predict(fact_1) | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | |
| # The claim is that Gregg Rolie and Rob Tyner, are not a keyboardist. |
| def program(): |
| | fact_1 = | Verify("Gregg | | Rolie | is not | a keyboardist.") | | |
| | -------- | -------------- | --- | ----- | ------- | ---------------- | --- | |
| | fact_2 = | Verify("Rob | | Tyner | is not | a keyboardist.") | | |
| | label = | Predict(fact_1 | | and | fact_2) | | | |
| # The claim is that Maria Esther Andion Bueno, not Jimmy Connors, is the player that |
| | is from | Brazil. | | | | | | |
| | ------- | ------- | --- | --- | --- | --- | --- | |
| def program(): |
| | fact_1 = | Verify("Maria | | Esther | Andion | Bueno is from | Brazil.") | |
| | -------- | -------------- | --- | ------- | ------- | ------------------ | --------- | |
| | fact_2 = | Verify("Jimmy | | Connors | is | not from Brazil.") | | |
| | label = | Predict(fact_1 | | and | fact_2) | | | |
| # The claim is that Vladimir Igorevich Arnold died after Georg Cantor. |
| def program(): |
| answer_1 = Question("When did Vladimir Igorevich Arnold die?") |
| | answer_2 | = Question("When | | | did Georg | Cantor die?") | | |
| | -------- | ------------------- | --- | --- | --------- | ------------- | --- | |
| | fact_1 = | Verify(f"{answer_1} | | | is after | {answer_2}.") | | |
| | label = | Predict(fact_1) | | | | | | |
| # The claim is that Barton Mine was halted by a natural disaster not Camlaren Mine. |
| def program(): |
| fact_1 = Verify("Barton Mine was halted by a natural disaster.") |
| fact_2 = Verify("Camlaren Mine was not halted by a natural disaster.") |
| | label = | Predict(fact_1 | | and | fact_2) | | | |
| | ------- | -------------- | --- | --- | ------- | --- | --- | |
| # The claim is that John O'Hara and Rabindranath Tagore are not the same nationality |
| . |
| def program(): |
| answer_1 = Question("What is the nationality of John O'Hara?") |
| answer_2 = Question("What is the nationality of Rabindranath Tagore?") |
| fact_1 = Verify(f"{answer_1} and {answer_2} are not the same nationality.") |
| | label = | Predict(fact_1) | | | | | | |
| | ------- | --------------- | --- | --- | --- | --- | --- | |
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|
| # The claim is that Thomas Loren Friedman has won more Pulitzer Prizes than Colson |
| Whitehead. |
| def program(): |
| answer_1 = Question("How many Pulitzer Prizes has Thomas Loren Friedman won?") |
| answer_2 = Question("How many Pulitzer Prizes has Colson Whitehead won?") |
| fact_1 = Verify(f"{answer_1} is more than {answer_2}.") |
| label = Predict(fact_1) |
| # The claim is that The model of car Trevor Bayne drives was introduced for model |
| year 2006. The Rookie of The Year in the 1997 CART season drives it in the |
| NASCAR Sprint Cup. |
| def program(): |
| answer_1 = Question("Which model of car is drived by Trevor Bayne?") |
| fact_1 = Verify(f"{answer_1} was introduced for model year 2006.") |
| answer_2 = Question("Who is the Rookie of The Year in the 1997 CART season?") |
| fact_2 = Verify(f"{answer_2} drives the model of car Trevor Bayne drives in the |
| NASCAR Sprint Cup.") |
| label = predict(fact_1 and fact_2) |
| # The claim is that <input_claim> |
| def program(): |
| Listing1: ThepromptusedforProgramGenerationforHOVER. |
| 7000 |
|
|
| '''Generate a python-like program that describes the reasoning steps required to |
| verify the claim step-by-step. You can call three functions in the program: 1. |
| Question () to answer a question; 2. Verify () to verify a simple claim; 3. |
| | Predict() | to predict | the | veracity | label.''' | | | |
| | --------- | ---------- | --- | -------- | --------- | --- | --- | |
| # The claim is that In 1959, former Chilean boxer Alfredo Cornejo Cuevas (born June |
| 6, 1933) won the gold medal in the welterweight division at the Pan American |
| Games (held in Chicago, United States, from August 27 to September 7) in Chicago |
| , United States, and the world amateur welterweight title in Mexico City. |
| def program(): |
| fact_1 = Verify("Alfredo Cornejo Cuevas was born in June 6, 1933.") |
| fact_2 = Verify("Alfredo Cornejo Cuevas won the gold medal in the welterweight |
| | division | at the Pan | American | Games | in | 1959.") | | |
| | -------- | ---------- | -------- | ----- | --- | ------- | --- | |
| fact_3 = Verify("The Pan American Games in 1959 was held in Chicago, United |
| | States, | from August | 27 to | September | 7.") | | | |
| | ------- | ----------- | ----- | --------- | ---- | --- | --- | |
| fact_4 = Verify("Alfredo Cornejo Cuevas won the world amateur welterweight title |
| | in Mexico | City.") | | | | | | |
| | --------- | -------------- | --- | ------ | --- | ------ | ----------- | |
| | label = | Predict(fact_1 | and | fact_2 | and | fact_3 | and fact_4) | |
| # The claim is that The Footwork FA12, which was intended to start the season, |
| finally debuted at the San Marino Grand Prix, a Formula One motor race held at |
| | Imola on | 28 April | 1991. | | | | | |
| | -------- | -------- | ----- | --- | --- | --- | --- | |
| def program(): |
| fact_1 = Verify("The Footwork FA12, which was intended to start the season.") |
| fact_2 = Verify("The Footwork FA12 finally debuted at the San Marino Grand Prix. |
| ") |
| fact_3 = Verify("The San Marino Grand Prix was a Formula One motor race held at |
| | Imola on | 28 April | 1991.") | | | | | |
| | -------- | -------------- | ------- | ------ | --- | ------- | --- | |
| | label = | Predict(fact_1 | and | fact_2 | and | fact_3) | | |
| # The claim is that SkyHigh Mount Dandenong (formerly Mount Dandenong Observatory) |
| is a restaurant located on top of Mount Dandenong, Victoria, Australia. |
| def program(): |
| fact_1 = Verify("SkyHigh Mount Dandenong is a restaurant located on top of Mount |
| | Dandenong, | Victoria, | Australia.") | | | | | |
| | ---------- | --------- | ------------ | --- | --- | --- | --- | |
| fact_2 = Verify("SkyHigh Mount Dandenong is formerly known as Mount Dandenong |
| Observatory.") |
| | label = | Predict(fact_1 | and | fact_2) | | | | |
| | ------- | -------------- | --- | ------- | --- | --- | --- | |
| # The claim is that Before the first Europeans arrived or copra companies leased it, |
| | Maupihaa | was home | to Inca's | in | ancient | times. | | |
| | -------- | -------- | --------- | --- | ------- | ------ | --- | |
| def program(): |
| fact_1 = Verify("Maupihaa was home to Inca's in ancient times.") |
| fact_2 = Verify("Maupihaa was home to Inca's before the first Europeans arrived |
| | or copra | companies | leased | it.") | | | | |
| | -------- | -------------- | ------ | ------- | --- | --- | --- | |
| | label = | Predict(fact_1 | and | fact_2) | | | | |
| # The claim is that Shulin, a 33.1288 km (12.7911 sq mi) land located in New Taipei |
| City, China, a country in East Asia, has a total population of 183,946 in |
| | December | 2018. | | | | | | |
| | -------- | ----- | --- | --- | --- | --- | --- | |
| def program(): |
| fact_1 = Verify("Shulin is a 33.1288 km (12.7911 sq mi) land located in New |
| | Taipei | City, China.") | | | | | | |
| | ------ | -------------- | --- | --- | --- | --- | --- | |
| fact_2 = Verify("Shulin has a total population of 183,946 in December 2018.") |
| | label = | Predict(fact_1 | and | fact_2) | | | | |
| | ------- | -------------- | --- | ------- | --- | --- | --- | |
| # The claim is that Sumo wrestler Toyozakura Toshiaki committed match-fixing, ending |
| | his career | in 2011 | that | started | in 1989. | | | |
| | ---------- | ------- | ---- | ------- | -------- | --- | --- | |
| def program(): |
| fact_1 = Verify("Toyozakura Toshiaki ended his career in 2011 that started in |
| 1989.") |
| | fact_2 | = Verify("Toyozakura | | Toshiaki | | is a | Sumo wrestler.") | |
| | ------ | -------------------- | --- | -------- | --- | ---- | ---------------- | |
| fact_3 = Verify("Toyozakura Toshiaki committed match-fixing.") |
| | label = | Predict(fact_1 | and | fact_2 | and | fact_3) | | |
| | ------- | -------------- | --- | ------ | --- | ------- | --- | |
| # The claim is that In 1959, former Chilean boxer Alfredo Cornejo Cuevas (born June |
| 6, 1933) won the gold medal in the welterweight division at the Pan American |
| Games (held in Chicago, United States, from August 27 to September 7) in Chicago |
| 7001 |
|
|
| , United States, and the world amateur welterweight title in Mexico City. |
| def program(): |
| fact_1 = Verify("Alfredo Cornejo Cuevas is a former Chilean boxer.") |
| fact_2 = Verify("Alfredo Cornejo won the gold medal in the welterweight division |
| | at the | Pan American | | Games.") | | | |
| | ------ | ------------ | --- | -------- | --- | --- | |
| fact_3 = Verify("The Pan American Games was held in Chicago, United States, from |
| | August | 27 to | September | 7.") | | | |
| | ------ | ----- | --------- | ---- | --- | --- | |
| fact_4 = Verify("Alfredo Cornejo won the world amateur welterweight title in |
| Mexico City.") |
| | label = | Predict(fact_1 | | and fact_2 | and fact_3 | and fact_4) | |
| | ------- | -------------- | --- | ---------- | ---------- | ----------- | |
| # The claim is that Adductor hiatus is associated with nine structures, seven of |
| | which enter | and | leave | through hiatus. | | | |
| | ----------- | --- | ----- | --------------- | --- | --- | |
| def program(): |
| fact_1 = Verify("Adductor hiatus is associated with nine structures.") |
| fact_2 = Verify("Seven of the nine structures associated with Adductor hiatus |
| | enter and | leave | through | hiatus.") | | | |
| | --------- | -------------- | ------- | ----------- | --- | --- | |
| | label = | Predict(fact_1 | | and fact_2) | | | |
| # The claim is that Ifor Bowen Lloyd was educated at Winchester (an independent |
| boarding school for boys in the British public school tradition) and Exeter |
| College, Oxford where he was a member of the Library Committee of the Oxford |
| Union Society, as well as, received a BA in Modern History in 1924. |
| def program(): |
| fact_1 = Verify("Ifor Bowen Lloyd was educated at Winchester and Exeter College, |
| Oxford.") |
| fact_2 = Verify("Winchester is an independent boarding school for boys in the |
| | British | public | school | tradition.") | | | |
| | ------- | ------ | ------ | ------------ | --- | --- | |
| fact_3 = Verify("While at Oxford, Ifor Bowen Lloyd was a member of the Library |
| | Committee | of the | Oxford | Union Society.") | | | |
| | --------- | ------ | ------ | ---------------- | --- | --- | |
| fact_4 = Verify("Ifor Bowen Lloyd received a BA in Modern History in 1924 at |
| Oxford.") |
| | label = | Predict(fact_1 | | and fact_2 | and fact_3 | and fact_4) | |
| | ------- | -------------- | --- | ---------- | ---------- | ----------- | |
| # The claim is that In the 2001 Stanley Cup playoffs Eastern Conference Semifinals |
| Devils' Elias scored and Maple Leafs' left Devils player Scott Neidermayer hurt. |
| def program(): |
| fact_1 = Verify("In the 2001 Stanley Cup playoffs Eastern Conference Semifinals |
| | Devils' | Elias | scored.") | | | | |
| | ------- | ----- | --------- | --- | --- | --- | |
| fact_2 = Verify("Maple Leafs' left Devils player Scott Neidermayer hurt.") |
| | label = | Predict(fact_1 | | and fact_2) | | | |
| | ------- | -------------- | --- | ----------- | --- | --- | |
| # The claim is that Teldenia helena is a moth first described in 1967 by Wilkinson. |
| def program(): |
| | fact_1 | = Verify("Teldenia | | helena | is a moth.") | | |
| | ------ | ------------------ | --- | ------ | ------------ | --- | |
| fact_2 = Verify("Teldenia helena was first described by Wilkinson in 1967.") |
| | label = | Predict(fact_1 | | and fact_2) | | | |
| | ------- | -------------- | --- | ----------- | --- | --- | |
| # The claim is that Born December 30, 1974, William Frick was a dark horse candidate |
| | in the | Maryland | House | of Delegates | appointment | process. | |
| | ------ | -------- | ----- | ------------ | ----------- | -------- | |
| def program(): |
| fact_1 = Verify("William Frick was born in December 30, 1974.") |
| fact_2 = Verify("William Frick was a dark horse candidate in the Maryland House |
| | of Delegates | appointment | | process.") | | | |
| | ------------ | -------------- | ------------- | ----------- | --- | --- | |
| | label = | Predict(fact_1 | | and fact_2) | | | |
| | # The claim | is that | <input_claim> | | | | |
| def program(): |
| | | Listing2: | ThepromptusedforProgramGenerationforFEVEROUS-S. | | | | |
| | --- | --------- | ----------------------------------------------- | --- | --- | --- | |
| 7002 |
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|
| ACL2023ResponsibleNLPChecklist |
| A Foreverysubmission: |
| 3 |
| (cid:3) A1. Didyoudescribethelimitationsofyourwork? |
| Line587-620 |
| 3 |
| (cid:3) A2. Didyoudiscussanypotentialrisksofyourwork? |
| Line626-630 |
| 3 |
| (cid:3) A3. Dotheabstractandintroductionsummarizethepaper’smainclaims? |
| Line67-86 |
| (cid:3) 7 A4. HaveyouusedAIwritingassistantswhenworkingonthispaper? |
| Leftblank. |
| 3 |
| B (cid:3) Didyouuseorcreatescientificartifacts? |
| Line327-352 |
| 3 |
| (cid:3) B1. Didyoucitethecreatorsofartifactsyouused? |
| Line328-329 |
| (cid:3) B2. Didyoudiscussthelicenseortermsforuseand/ordistributionofanyartifacts? |
| Notapplicable. Thedatasetsusedinthispaperarepubliclyavailabledatasetsfromexistingworks. |
| 3 |
| (cid:3) B3. Didyoudiscussifyouruseofexistingartifact(s)wasconsistentwiththeirintendeduse,provided |
| thatitwasspecified? Fortheartifactsyoucreate,doyouspecifyintendeduseandwhetherthatis |
| compatiblewiththeoriginalaccessconditions(inparticular,derivativesofdataaccessedforresearch |
| purposesshouldnotbeusedoutsideofresearchcontexts)? |
| Line327-344 |
| (cid:3) B4. Didyoudiscussthestepstakentocheckwhetherthedatathatwascollected/usedcontainsany |
| informationthatnamesoruniquelyidentifiesindividualpeopleoroffensivecontent,andthesteps |
| takentoprotect/anonymizeit? |
| Notapplicable. Leftblank. |
| (cid:3) B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and |
| linguisticphenomena,demographicgroupsrepresented,etc.? |
| Notapplicable. Leftblank. |
| 3 |
| (cid:3) B6. Didyoureportrelevantstatisticslikethenumberofexamples,detailsoftrain/test/devsplits, |
| etc. forthedatathatyouused/created? Evenforcommonly-usedbenchmarkdatasets,includethe |
| numberofexamplesintrain/validation/testsplits,astheseprovidenecessarycontextforareader |
| tounderstandexperimentalresults. Forexample,smalldifferencesinaccuracyonlargetestsetsmay |
| besignificant,whileonsmalltestsetstheymaynotbe. |
| Line327-344 |
| 3 |
| C (cid:3) Didyouruncomputationalexperiments? |
| Section4 |
| 3 |
| (cid:3) C1. Didyoureportthenumberofparametersinthemodelsused, thetotalcomputationalbudget |
| (e.g.,GPUhours),andcomputinginfrastructureused? |
| Figure4;AppendixA |
| TheResponsibleNLPChecklistusedatACL2023isadoptedfromNAACL2022,withtheadditionofaquestiononAIwriting |
| assistance. |
| 7003 |
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|
| 3 |
| (cid:3) C2. Did you discuss the experimental setup, including hyperparameter search and best-found |
| hyperparametervalues? |
| AppendixA |
| 3 |
| (cid:3) C3. Didyoureportdescriptivestatisticsaboutyourresults(e.g.,errorbarsaroundresults,summary |
| statisticsfromsetsofexperiments),andisittransparentwhetheryouarereportingthemax,mean, |
| etc. orjustasinglerun? |
| AppendixA |
| (cid:3) C4. Ifyouusedexistingpackages(e.g.,forpreprocessing,fornormalization,orforevaluation),did |
| youreporttheimplementation,model,andparametersettingsused(e.g.,NLTK,Spacy,ROUGE, |
| etc.)? |
| Notapplicable. Leftblank. |
| 7 |
| D (cid:3) Didyouusehumanannotators(e.g.,crowdworkers)orresearchwithhumanparticipants? |
| Leftblank. |
| (cid:3) D1. Did you report the full text of instructions given to participants, including e.g., screenshots, |
| disclaimersofanyriskstoparticipantsorannotators,etc.? |
| Notapplicable. Leftblank. |
| (cid:3) D2. Didyoureportinformationabouthowyourecruited(e.g.,crowdsourcingplatform,students) |
| andpaidparticipants,anddiscussifsuchpaymentisadequategiventheparticipants’demographic |
| (e.g.,countryofresidence)? |
| Notapplicable. Leftblank. |
| (cid:3) D3. Did you discuss whether and how consent was obtained from people whose data you’re |
| using/curating? For example, if you collected data via crowdsourcing, did your instructions to |
| crowdworkersexplainhowthedatawouldbeused? |
| Notapplicable. Leftblank. |
| (cid:3) D4. Wasthedatacollectionprotocolapproved(ordeterminedexempt)byanethicsreviewboard? |
| Notapplicable. Leftblank. |
| (cid:3) D5. Didyoureportthebasicdemographicandgeographiccharacteristicsoftheannotatorpopulation |
| thatisthesourceofthedata? |
| Notapplicable. Leftblank. |
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