metadata
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
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:
{
"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.