{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"big-bench-hard-bbh","slug":"big-bench-hard-bbh","name":"BIG-Bench Hard (BBH)","type":"dataset","url":"https://huggingface.co/datasets/lukaemon/bbh","page_url":"https://unfragile.ai/big-bench-hard-bbh","categories":["testing-quality","rag-knowledge"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"big-bench-hard-bbh__cap_0","uri":"capability://data.processing.analysis.chain.of.thought.reasoning.evaluation.with.few.shot.examples","name":"chain-of-thought reasoning evaluation with few-shot examples","description":"Provides curated few-shot chain-of-thought (CoT) exemplars for 23 hard reasoning tasks, enabling models to learn structured step-by-step problem decomposition through in-context learning. Each task includes 3-5 hand-crafted examples showing intermediate reasoning steps, allowing models to adopt explicit reasoning patterns without fine-tuning. The dataset leverages prompt engineering patterns where models observe reasoning trajectories before solving novel instances.","intents":["Evaluate whether my model can improve performance on hard reasoning tasks by learning from CoT examples","Benchmark my model's few-shot reasoning capability against frontier models using standardized hard tasks","Understand which reasoning patterns (arithmetic, logic, spatial) my model struggles with most"],"best_for":["ML researchers evaluating frontier model capabilities on reasoning","Teams developing reasoning-focused LLMs and wanting standardized hard benchmarks","Practitioners testing whether prompt engineering with CoT improves their model's performance"],"limitations":["Few-shot examples are static and hand-crafted — no automatic generation or adaptation to model-specific weaknesses","CoT format assumes models can follow structured reasoning; doesn't test implicit reasoning or intuition-based problem solving","Limited to 23 tasks — may not cover all reasoning domains (e.g., creative reasoning, social reasoning)","Examples are English-only; no multilingual reasoning evaluation"],"requires":["Hugging Face Datasets library (transformers >= 4.0)","Python 3.7+","LLM capable of few-shot in-context learning (GPT-3.5+, Claude, Llama 2+, or equivalent)"],"input_types":["text (problem statements)","structured JSON (task metadata, few-shot examples)"],"output_types":["text (model reasoning steps and final answers)","structured JSON (task results, accuracy metrics)"],"categories":["data-processing-analysis","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_1","uri":"capability://data.processing.analysis.multi.domain.reasoning.task.stratification","name":"multi-domain reasoning task stratification","description":"Organizes 23 tasks across distinct reasoning domains (algorithmic, arithmetic, logical, causal, spatial) with consistent evaluation structure, enabling fine-grained analysis of model strengths and weaknesses by reasoning type. Each task is independently evaluable with its own test set and metrics, allowing researchers to identify which reasoning modalities their models excel or fail at. The stratification enables targeted model development and capability analysis.","intents":["Identify which reasoning domains my model is weakest in so I can focus improvement efforts","Compare my model's performance across reasoning types to understand capability gaps","Evaluate whether my model improvements generalize across reasoning domains or are task-specific"],"best_for":["Model developers doing capability analysis and debugging reasoning failures","Researchers studying which reasoning types are hardest for LLMs","Teams building specialized reasoning modules and needing domain-specific evaluation"],"limitations":["Task domains are predefined and fixed — no ability to add custom reasoning categories","No cross-domain transfer analysis built-in; requires manual correlation analysis","Domain labels are coarse-grained; some tasks may span multiple reasoning types","No task difficulty ranking within domains — all tasks treated as equally hard"],"requires":["Hugging Face Datasets library","Python 3.7+","Evaluation framework capable of per-task metric computation"],"input_types":["text (task descriptions and problem statements)"],"output_types":["structured data (per-domain accuracy, per-task results, reasoning type labels)"],"categories":["data-processing-analysis","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_10","uri":"capability://data.processing.analysis.frontier.model.capability.benchmarking","name":"frontier model capability benchmarking","description":"Designed specifically to evaluate frontier language models (GPT-4, Claude, Llama 2+, etc.) on hard reasoning tasks where initial model performance was below human level, enabling measurement of model improvement over time and comparison of frontier model capabilities. The dataset enables researchers to track whether new model releases improve on hard reasoning and to identify reasoning capabilities that remain unsolved. Results are directly comparable across models because of standardized evaluation infrastructure.","intents":["Benchmark my frontier model against published results on hard reasoning tasks","Track whether new model versions improve performance on hard reasoning","Compare my model's reasoning capabilities against competing frontier models"],"best_for":["Frontier model developers evaluating new model releases on hard reasoning","Researchers publishing model results and needing standardized hard benchmarks","Teams comparing frontier model capabilities and identifying reasoning gaps"],"limitations":["Benchmark is static; no continuous updates as new reasoning task types emerge","Results may become outdated as frontier models improve and surpass human performance","No task difficulty adjustment as models improve; tasks remain fixed","Benchmark may not capture emerging reasoning capabilities that weren't anticipated at curation time"],"requires":["Hugging Face Datasets library","Python 3.7+","Access to frontier LLM (GPT-4, Claude, Llama 2+, or equivalent)","Sufficient API quota or local model resources for comprehensive evaluation"],"input_types":["structured JSON (task definitions, few-shot examples)"],"output_types":["structured data (per-task accuracy, aggregate metrics, model comparison results)"],"categories":["data-processing-analysis","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_11","uri":"capability://automation.workflow.reproducible.model.evaluation.and.result.comparison","name":"reproducible model evaluation and result comparison","description":"Enables reproducible evaluation across different models and research groups by providing standardized task definitions, test sets, evaluation metrics, and result aggregation. The dataset structure ensures that different teams can run identical evaluations and compare results directly, reducing evaluation variance and enabling fair model comparison. Standardized evaluation infrastructure supports publishing reproducible results and enables meta-analysis across multiple model evaluations.","intents":["Run reproducible evaluation of my model and publish results that can be compared against other models","Verify that published benchmark results are reproducible and not artifacts of evaluation differences","Compare my model's performance against other models using identical evaluation methodology"],"best_for":["Researchers publishing model results and needing reproducible evaluation","Teams validating published benchmark results and ensuring reproducibility","Practitioners comparing multiple models and wanting fair, standardized evaluation"],"limitations":["Reproducibility depends on identical model versions and API behavior; model updates may change results","Evaluation is deterministic but model outputs may vary due to temperature, sampling, or API changes","No built-in support for statistical significance testing or confidence intervals","Reproducibility assumes identical prompt formatting and input preprocessing"],"requires":["Hugging Face Datasets library","Python 3.7+","Identical LLM version and API configuration","Evaluation framework with standardized metric computation"],"input_types":["structured JSON (standardized task definitions, test sets)"],"output_types":["structured data (standardized evaluation results, metrics, comparison data)"],"categories":["automation-workflow","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_2","uri":"capability://data.processing.analysis.human.baseline.performance.anchoring","name":"human-baseline performance anchoring","description":"Includes human rater performance data for all 23 tasks, establishing ground-truth difficulty calibration and enabling measurement of model-vs-human performance gaps. Tasks were specifically selected where initial model performance fell below human median (50th percentile), creating a calibrated hard benchmark. Human baselines enable researchers to quantify progress toward human-level reasoning and identify tasks where models have surpassed human performance.","intents":["Measure how close my model is to human-level performance on hard reasoning tasks","Identify which tasks my model has already surpassed humans on, and which remain unsolved","Calibrate task difficulty and validate that my benchmark is actually measuring hard reasoning"],"best_for":["Researchers publishing frontier model results and needing human baselines for comparison","Teams evaluating whether their models have achieved human-level reasoning on specific domains","Practitioners validating benchmark difficulty and ensuring tasks are appropriately challenging"],"limitations":["Human baselines are static snapshots from original BIG-Bench evaluation; no continuous human re-evaluation","Human performance may reflect annotation quality and rater expertise variation — not true 'human ceiling'","No inter-rater agreement or confidence intervals provided; single aggregate score per task","Human baselines may be outdated as models improve and task difficulty perception shifts"],"requires":["Hugging Face Datasets library","Python 3.7+","Access to human performance metadata in dataset"],"input_types":["structured JSON (human performance scores, task metadata)"],"output_types":["numerical metrics (human accuracy, model-vs-human gap, performance percentiles)"],"categories":["data-processing-analysis","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_3","uri":"capability://automation.workflow.standardized.multi.task.evaluation.harness","name":"standardized multi-task evaluation harness","description":"Provides consistent evaluation infrastructure across 23 heterogeneous reasoning tasks with unified input/output schemas, metrics computation, and result aggregation. Each task includes standardized test sets, answer formats, and evaluation functions, enabling researchers to run comprehensive benchmarks with a single evaluation script. The harness abstracts task-specific complexity and enables reproducible, comparable results across models and research groups.","intents":["Run a comprehensive evaluation of my model across all 23 hard reasoning tasks with a single command","Compare my model's results against published benchmarks using identical evaluation methodology","Reproduce published results and verify my model improvements are real and not artifacts of evaluation differences"],"best_for":["ML researchers publishing model results and needing reproducible evaluation","Teams benchmarking multiple models and wanting consistent evaluation across all tasks","Practitioners validating model improvements and needing standardized metrics"],"limitations":["Evaluation harness is read-only; no ability to customize metrics or add task-specific evaluation logic","No built-in support for streaming evaluation or distributed evaluation across multiple GPUs","Metrics are aggregate (accuracy, F1); no fine-grained error analysis or per-example debugging","No automatic hyperparameter tuning or prompt optimization — evaluation assumes fixed prompts"],"requires":["Hugging Face Datasets library","Python 3.7+","LLM API access (OpenAI, Anthropic, Hugging Face Inference API, or local model)","Evaluation framework (e.g., lm-eval, custom evaluation script)"],"input_types":["structured JSON (task definitions, test sets, few-shot examples)"],"output_types":["structured data (per-task accuracy, aggregate metrics, result JSON)"],"categories":["automation-workflow","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_4","uri":"capability://planning.reasoning.algorithmic.reasoning.task.evaluation","name":"algorithmic reasoning task evaluation","description":"Includes algorithmic reasoning tasks (e.g., sorting, graph traversal, dynamic programming) that test whether models can learn and apply computational algorithms through few-shot examples. Tasks present problem descriptions and expect models to reason through algorithmic steps, testing whether models can generalize algorithmic patterns beyond memorized examples. This capability isolates algorithmic reasoning from knowledge retrieval or common-sense reasoning.","intents":["Test whether my model can learn and apply algorithms from few-shot examples without explicit training","Evaluate my model's ability to reason about computational complexity and algorithmic correctness","Identify whether my model struggles with specific algorithm types (sorting, searching, graph algorithms)"],"best_for":["Researchers studying whether LLMs can learn algorithmic reasoning through in-context learning","Teams developing code generation models and wanting to evaluate algorithmic reasoning capability","Practitioners testing whether models can solve programming interview-style algorithmic problems"],"limitations":["Algorithmic tasks are limited to ~5-7 tasks; may not cover all algorithm families comprehensively","Tasks are presented in natural language rather than pseudocode or formal specifications, introducing ambiguity","No intermediate step evaluation; only final answer correctness is measured","Tasks may conflate algorithmic reasoning with language understanding of problem descriptions"],"requires":["Hugging Face Datasets library","Python 3.7+","LLM capable of multi-step reasoning (GPT-3.5+, Claude, Llama 2+)"],"input_types":["text (algorithm problem descriptions, few-shot examples)"],"output_types":["text (algorithm steps and final answer)","numerical (accuracy on algorithmic reasoning tasks)"],"categories":["planning-reasoning","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_5","uri":"capability://planning.reasoning.arithmetic.and.mathematical.reasoning.evaluation","name":"arithmetic and mathematical reasoning evaluation","description":"Includes multi-step arithmetic and mathematical reasoning tasks (e.g., word problems, numerical reasoning, mathematical deduction) that test whether models can perform accurate calculations and apply mathematical reasoning through few-shot examples. Tasks range from basic arithmetic to more complex mathematical inference, isolating numerical reasoning from language understanding. Evaluation measures both intermediate calculation accuracy and final answer correctness.","intents":["Evaluate my model's ability to perform multi-step arithmetic and mathematical reasoning accurately","Test whether my model can learn mathematical problem-solving patterns from few-shot examples","Identify whether my model makes calculation errors or reasoning mistakes on mathematical tasks"],"best_for":["Researchers studying LLM mathematical reasoning and arithmetic accuracy","Teams developing models for STEM education or technical domains requiring math","Practitioners evaluating whether models can solve word problems and mathematical reasoning tasks"],"limitations":["Arithmetic tasks may be limited in scope (e.g., no calculus, advanced algebra, or symbolic math)","Tasks are presented in natural language; parsing ambiguity may conflate math reasoning with language understanding","No intermediate step verification; only final numerical answer is checked","No support for symbolic math or formal mathematical notation; all tasks use natural language"],"requires":["Hugging Face Datasets library","Python 3.7+","LLM capable of numerical reasoning (GPT-3.5+, Claude, Llama 2+)"],"input_types":["text (word problems, mathematical reasoning prompts, few-shot examples)"],"output_types":["text (reasoning steps and numerical answer)","numerical (accuracy on arithmetic tasks)"],"categories":["planning-reasoning","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_6","uri":"capability://planning.reasoning.logical.deduction.and.inference.evaluation","name":"logical deduction and inference evaluation","description":"Includes logical reasoning tasks (e.g., syllogisms, logical deduction, constraint satisfaction) that test whether models can perform formal logical inference through few-shot examples. Tasks present logical premises and expect models to derive correct conclusions, testing whether models can apply logical rules consistently. This capability isolates formal logical reasoning from common-sense reasoning or knowledge retrieval.","intents":["Evaluate my model's ability to perform formal logical deduction and inference","Test whether my model can learn logical reasoning patterns from few-shot examples","Identify whether my model makes logical fallacies or inconsistent reasoning"],"best_for":["Researchers studying LLM logical reasoning and formal inference capabilities","Teams developing models for knowledge representation and reasoning systems","Practitioners testing whether models can solve logic puzzles and constraint satisfaction problems"],"limitations":["Logical tasks may be limited to simple syllogisms and basic deduction; no complex formal logic","Tasks are presented in natural language rather than formal logical notation, introducing ambiguity","No intermediate inference step verification; only final logical conclusion is checked","No support for probabilistic reasoning or uncertainty; all tasks assume deterministic logic"],"requires":["Hugging Face Datasets library","Python 3.7+","LLM capable of logical reasoning (GPT-3.5+, Claude, Llama 2+)"],"input_types":["text (logical premises, deduction prompts, few-shot examples)"],"output_types":["text (logical reasoning steps and conclusion)","numerical (accuracy on logical reasoning tasks)"],"categories":["planning-reasoning","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_7","uri":"capability://planning.reasoning.causal.reasoning.and.judgment.evaluation","name":"causal reasoning and judgment evaluation","description":"Includes causal reasoning tasks that test whether models can identify causal relationships, make causal inferences, and reason about cause-and-effect through few-shot examples. Tasks present scenarios and expect models to identify causal mechanisms or predict causal outcomes, testing whether models can reason about causality beyond correlation. This capability isolates causal reasoning from statistical reasoning or common-sense knowledge.","intents":["Evaluate my model's ability to identify and reason about causal relationships","Test whether my model can learn causal reasoning patterns from few-shot examples","Identify whether my model confuses correlation with causation or makes causal reasoning errors"],"best_for":["Researchers studying LLM causal reasoning and causal inference capabilities","Teams developing models for scientific reasoning or causal analysis","Practitioners testing whether models can reason about cause-and-effect in complex scenarios"],"limitations":["Causal tasks may be limited to simple cause-effect relationships; no complex causal networks","Tasks are presented in natural language; causal ambiguity may conflate causal reasoning with language understanding","No intermediate causal inference step verification; only final causal judgment is checked","No support for probabilistic causality or counterfactual reasoning; all tasks assume deterministic causality"],"requires":["Hugging Face Datasets library","Python 3.7+","LLM capable of causal reasoning (GPT-3.5+, Claude, Llama 2+)"],"input_types":["text (causal scenarios, causal reasoning prompts, few-shot examples)"],"output_types":["text (causal reasoning and judgment)","numerical (accuracy on causal reasoning tasks)"],"categories":["planning-reasoning","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_8","uri":"capability://planning.reasoning.spatial.reasoning.and.visualization.evaluation","name":"spatial reasoning and visualization evaluation","description":"Includes spatial reasoning tasks (e.g., mental rotation, spatial visualization, geometric reasoning) that test whether models can reason about spatial relationships and visualize spatial configurations through few-shot examples. Tasks present spatial descriptions and expect models to reason about spatial transformations or configurations, testing whether models can build and manipulate mental spatial models. This capability isolates spatial reasoning from visual perception or geometric knowledge.","intents":["Evaluate my model's ability to reason about spatial relationships and mental rotation","Test whether my model can learn spatial reasoning patterns from few-shot examples","Identify whether my model struggles with spatial visualization or geometric reasoning"],"best_for":["Researchers studying LLM spatial reasoning and mental rotation capabilities","Teams developing models for robotics, navigation, or spatial understanding","Practitioners testing whether models can solve spatial reasoning puzzles and geometric problems"],"limitations":["Spatial tasks are presented in text only; no visual images or diagrams provided","Tasks may be limited to simple spatial relationships; no complex 3D spatial reasoning","Text-based spatial descriptions may be ambiguous or require strong spatial visualization ability","No intermediate spatial reasoning step verification; only final spatial answer is checked"],"requires":["Hugging Face Datasets library","Python 3.7+","LLM capable of spatial reasoning (GPT-3.5+, Claude, Llama 2+)"],"input_types":["text (spatial descriptions, spatial reasoning prompts, few-shot examples)"],"output_types":["text (spatial reasoning and answer)","numerical (accuracy on spatial reasoning tasks)"],"categories":["planning-reasoning","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__cap_9","uri":"capability://text.generation.language.few.shot.prompt.engineering.and.optimization","name":"few-shot prompt engineering and optimization","description":"Enables researchers to experiment with few-shot prompt engineering by providing curated exemplars for each task that can be modified, reordered, or augmented to test prompt sensitivity and optimization strategies. The dataset structure supports prompt template variation, exemplar selection strategies, and in-context learning optimization without requiring task re-annotation. Researchers can measure how prompt engineering choices affect model performance on hard reasoning tasks.","intents":["Experiment with different few-shot exemplars and prompt formats to optimize model performance","Test whether my model's performance is sensitive to exemplar order, format, or selection strategy","Develop and validate prompt engineering techniques for hard reasoning tasks"],"best_for":["Prompt engineers and researchers optimizing few-shot learning strategies","Teams developing prompt engineering best practices for reasoning tasks","Practitioners testing whether prompt optimization can improve model performance on hard tasks"],"limitations":["Exemplars are hand-crafted and fixed; no automatic exemplar generation or selection","No built-in support for prompt template optimization or hyperparameter tuning","Prompt engineering effects are task-specific; generalization across tasks is unclear","No guidance on optimal exemplar count, format, or selection strategy"],"requires":["Hugging Face Datasets library","Python 3.7+","LLM API access for prompt experimentation","Evaluation framework for measuring prompt engineering effects"],"input_types":["text (few-shot exemplars, prompt templates)"],"output_types":["text (model outputs with different prompts)","numerical (performance metrics for different prompt strategies)"],"categories":["text-generation-language","benchmark-dataset"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"big-bench-hard-bbh__headline","uri":"capability://testing.quality.benchmark.dataset.for.evaluating.language.model.reasoning","name":"benchmark dataset for evaluating language model reasoning","description":"A curated dataset featuring 23 challenging tasks designed to assess the reasoning capabilities of language models, focusing on algorithmic reasoning, logical deduction, and more, pushing the limits of current AI models beyond simple knowledge retrieval.","intents":["best dataset for language model evaluation","benchmark for AI reasoning tasks","datasets for testing model limits","challenging tasks for AI evaluation","language model reasoning benchmark"],"best_for":["evaluating AI reasoning","testing model improvements"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality","rag-knowledge"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":59,"verified":false,"data_access_risk":"low","permissions":["Hugging Face Datasets library (transformers >= 4.0)","Python 3.7+","LLM capable of few-shot in-context learning (GPT-3.5+, Claude, Llama 2+, or equivalent)","Hugging Face Datasets library","Evaluation framework capable of per-task metric computation","Access to frontier LLM (GPT-4, Claude, Llama 2+, or equivalent)","Sufficient API quota or local model resources for comprehensive evaluation","Identical LLM version and API configuration","Evaluation framework with standardized metric computation","Access to human performance metadata in dataset"],"failure_modes":["Few-shot examples are static and hand-crafted — no automatic generation or adaptation to model-specific weaknesses","CoT format assumes models can follow structured reasoning; 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