| --- |
| task_categories: |
| - text-classification |
| language: |
| - en |
| pretty_name: Big Decisions |
| size_categories: |
| - 1M<n<10M |
| configs: |
| - config_name: default |
| default: true |
| data_files: |
| - split: train |
| path: train.parquet |
| - split: dev |
| path: dev.parquet |
| - split: test |
| path: test.parquet |
| --- |
| |
| # Big Decisions |
|
|
| Big Decisions brings together more than four million examples from 74 datasets, covering classification, language understanding, reasoning, policy, tool use, preference, and other decision tasks. Each example pairs a context with a question, possible answers, and a target answer. |
|
|
| ## Dataset splits |
|
|
| | Split | Examples | |
| |---|---:| |
| | Train | 3,632,283 | |
| | Dev | 326,539 | |
| | Test | 86,931 | |
| | **Total** | **4,045,753** | |
|
|
| ## Load the data |
|
|
| ```python |
| from datasets import load_dataset |
| import json |
| |
| data = load_dataset("ELBennett/big-decisions") |
| row = data["train"][0] |
| questions = json.loads(row["questions_json"]) |
| ``` |
|
|
| Each row's `state` is the context. Its `questions_json` contains one or more questions. A question has a consistent choice-based structure: |
|
|
| ```json |
| { |
| "type": "choice", |
| "instructions": "Which category does this text belong to?", |
| "criteria": ["Album", "Animal", "Artist", "Company"], |
| "label": 3 |
| } |
| ``` |
|
|
| `criteria` lists the possible answers. `label` is a zero-based index into that list, so `3` selects `"Company"`. Some examples include `label_probs`, a probability for each answer, or `choice_ranks`, the rank of each answer in ordinal tasks. Binary and ordinal decisions use the same choice format. |
|
|
| Rows also include a task `domain` and source identifiers. The data is tokenizer-neutral; use the tokenizer appropriate for your model. |
|
|
| ## Included source datasets |
|
|
| Counts below are examples retained in this release after deduplication. |
|
|
| | Task area | Dataset | Examples | Original source | |
| |---|---|---:|---| |
| | Aspect and sentiment classification | ACOS (`acos`) | 11,971 | NUSTM/ACOS | |
| | Exam | AGIEval (`agieval`) | 7,082 | hails/agieval-* | |
| | Search relevance | Amazon ESCI (`amazon_esci`) | 47,658 | tasksource/esci or amazon-science/esci-data | |
| | Semantics and inference | ANLI (`anli`) | 169,072 | facebook/anli | |
| | Tool use | API-Bank (`api_bank`) | 10,908 | liminghao1630/API-Bank | |
| | Exam | ARC-Challenge (`arc_challenge`) | 3,710 | allenai/ai2_arc:ARC-Challenge | |
| | Exam | ARC-Easy (`arc_easy`) | 2,251 | allenai/ai2_arc:ARC-Easy | |
| | Intent classification | BANKING77 (`banking77`) | 13,071 | PolyAI/banking77 | |
| | Reasoning benchmark | BBH (`bbh`) | 5,499 | lukaemon/bbh | |
| | Decision-making | BEV Decision Mix (`bev_decision`) | 501,927 | avbiswas/bev-decision | |
| | Tool use | BFCL (`bfcl`) | 1,906 | gorilla-llm/gorilla berkeley-function-call-leaderboard | |
| | Reasoning benchmark | BIG-Bench (`bigbench`) | 451,563 | tasksource/bigbench | |
| | Reading comprehension | BoolQ (`boolq`) | 9,427 | google/boolq | |
| | Humor and language | BPoMP (`bpomp`) | 23,532 | zenodo.org/records/7299879 | |
| | Search relevance | BRIGHT (`bright`) | 937 | xlangai/BRIGHT | |
| | Causal reasoning | CLadder (`cladder`) | 8,917 | causalNLP/cladder (HF mirror or GitHub release) | |
| | Intent classification | CLINC150+OOS (`clinc150`) | 28,266 | clinc/clinc_oos | |
| | Semantics and inference | ContractNLI (`contract_nli`) | 7,837 | tasksource/contract-nli:contractnli_a | |
| | Code reasoning | CRUXEval (`cruxeval`) | 800 | cruxeval-org/cruxeval | |
| | Topic classification | DBpedia-14 (`dbpedia14`) | 99,988 | fancyzhx/dbpedia_14 | |
| | Policy | Domain decisions (`domain_decisions`) | 122,438 | altslate/certo-decisions-v2:domain_generator | |
| | Policy | Executable policies (`executable_policies`) | 96,318 | altslate/certo-decisions-v2:own_policy_generator | |
| | Financial sentiment | FinEntity (`finentity`) | 1,985 | yixuantt/FinEntity | |
| | Forecasting | ForecastBench (`forecastbench`) | 4,562 | forecastingresearch/forecastbench-datasets | |
| | Grammar and acceptability | GLUE CoLA (`glue_cola`) | 9,537 | nyu-mll/glue:cola | |
| | Semantics and inference | GLUE MNLI (`glue_mnli`) | 412,129 | nyu-mll/glue:mnli | |
| | Semantics and inference | GLUE MRPC (`glue_mrpc`) | 4,076 | nyu-mll/glue:mrpc | |
| | Semantics and inference | GLUE QNLI (`glue_qnli`) | 110,184 | nyu-mll/glue:qnli | |
| | Semantic similarity | GLUE QQP (`glue_qqp`) | 403,904 | nyu-mll/glue:qqp | |
| | Semantics and inference | GLUE RTE (`glue_rte`) | 2,765 | nyu-mll/glue:rte | |
| | Sentiment classification | GLUE SST-2 (`glue_sst2`) | 67,845 | nyu-mll/glue:sst2 | |
| | Semantic similarity | GLUE STS-B (`glue_stsb`) | 7,197 | nyu-mll/glue:stsb | |
| | Semantics and inference | GLUE WNLI (`glue_wnli`) | 706 | nyu-mll/glue:wnli | |
| | Emotion classification | GoEmotions (`go_emotions`) | 428,892 | google-research-datasets/go_emotions | |
| | Exam | GPQA (`gpqa`) | 439 | hendrydong/gpqa_main_mc + gpqa_diamond_mc | |
| | Mathematical reasoning | GSM8K (`gsm8k`) | 8,792 | openai/gsm8k | |
| | Group preference | Habermas Machine (`habermas`) | 7,710 | google-deepmind/habermas_machine | |
| | Commonsense reasoning | HellaSwag (`hellaswag`) | 49,947 | Rowan/hellaswag | |
| | Tool use | Home appliance simulator (`home_appliance`) | 851 | apolinario/decision-index home_appliance.py | |
| | Fact verification | HoVer (`hover`) | 22,005 | vincentkoc/hover-parquet | |
| | Humor and language | Humicroedit (`humicroedit`) | 8,325 | cs.rochester.edu humicroedit SemEval-2020 task-7 | |
| | Sarcasm detection | iSarcasmEval (`isarcasm`) | 4,844 | iabufarha/iSarcasmEval | |
| | Probability and belief | Known posterior (`known_posterior`) | 120,000 | altslate/certo-decisions-v2:genworld | |
| | Exam | LogiQA (`logiqa`) | 16,429 | lgw863/LogiQA-dataset | |
| | Exam | MMLU (remaining) (`mmlu_full`) | 11,956 | cais/mmlu | |
| | Exam | MMLU high school (`mmlu_high_school`) | 3,850 | cais/mmlu | |
| | Exam | MMLU-Pro (`mmlu_pro`) | 11,972 | TIGER-Lab/MMLU-Pro | |
| | Semantics and inference | MultiNLI (`multinli`) | 239,992 | nyu-mll/multi_nli | |
| | Multi-step reasoning | MuSR (`musr`) | 828 | TAUR-Lab/MuSR | |
| | Humor and language | New Yorker (`new_yorker`) | 11,674 | jmhessel/newyorker_caption_contest | |
| | Semantics and inference | NLI4CT (`nli4ct`) | 7,376 | ai-systems/Task-2-SemEval-2024 | |
| | Security classification | PhishNChips (`phishnchips`) | 2,013 | AreLit/PhishNChips | |
| | Music understanding | POP909-CL (`pop909_cl`) | 24,000 | AndyWeasley2004/POP909-CL-Dataset | |
| | Reading comprehension | RACE (`race`) | 97,734 | ehovy/race | |
| | Factuality detection | RAGTruth (`ragtruth`) | 17,722 | wandb/RAGTruth-processed | |
| | Exam | ReClor (`reclor`) | 5,123 | metaeval/reclor | |
| | Model routing | RouterBench (`routerbench`) | 36,483 | withmartian/routerbench | |
| | Exam | SAT analogies (`sat_analogies`) | 374 | willwhitney/6.863-final-project | |
| | Multi-step reasoning | SATA-Bench (`sata_bench`) | 1,605 | sata-bench/sata-bench | |
| | Semantics and inference | SuperGLUE AX-b (`sg_axb`) | 1,100 | aps/super_glue:axb | |
| | Semantics and inference | SuperGLUE AX-g (`sg_axg`) | 356 | aps/super_glue:axg | |
| | Reading comprehension | SuperGLUE BoolQ (`sg_boolq`) | 12,697 | aps/super_glue:boolq | |
| | Semantics and inference | SuperGLUE CommitmentBank (`sg_cb`) | 306 | aps/super_glue:cb | |
| | Causal reasoning | SuperGLUE COPA (`sg_copa`) | 500 | aps/super_glue:copa | |
| | Reading comprehension | SuperGLUE MultiRC (`sg_multirc`) | 32,028 | aps/super_glue:multirc | |
| | Reading comprehension | SuperGLUE ReCoRD (`sg_record`) | 110,656 | aps/super_glue:record | |
| | Semantics and inference | SuperGLUE RTE (`sg_rte`) | 76 | aps/super_glue:rte | |
| | Lexical semantics | SuperGLUE WiC (`sg_wic`) | 6,066 | aps/super_glue:wic | |
| | Coreference resolution | SuperGLUE WSC (`sg_wsc`) | 606 | aps/super_glue:wsc.fixed | |
| | Dialogue intent classification | SGD/SGD-X (`sgd`) | 10,890 | google-research-datasets/dstc8-schema-guided-dialogue | |
| | Tool use | ToolRet (`toolret`) | 7,460 | mangopy/ToolRet-Queries | |
| | Stance classification | VAST (`vast`) | 12,048 | emilyallaway/zero-shot-stance | |
| | Tool use | When2Call (`when2call`) | 18,401 | nvidia/When2Call | |
| | Commonsense reasoning | WinoGrande (`winogrande`) | 41,659 | allenai/winogrande | |
| |
| ## Data quality note |
| |
| 925 malformed source records with only one possible answer were excluded because they do not form a choice task. No alternative answers were invented. |
| |
