{"passport":{"unfragile":{"@version":"1.0","version":"2026-05","artifact":{"id":"mmlu-massive-multitask-language-understanding","slug":"mmlu-massive-multitask-language-understanding","name":"MMLU (Massive Multitask Language Understanding)","type":"benchmark","url":"https://huggingface.co/datasets/cais/mmlu","page_url":"https://unfragile.ai/mmlu-massive-multitask-language-understanding","categories":["testing-quality"],"tags":[],"pricing":{"model":"free","free":true,"starting_price":null},"status":"active","verified":false},"capabilities":[{"id":"mmlu-massive-multitask-language-understanding__cap_0","uri":"capability://data.processing.analysis.multi.subject.knowledge.evaluation.across.57.academic.domains","name":"multi-subject knowledge evaluation across 57 academic domains","description":"Evaluates LLM knowledge breadth and depth across 57 distinct academic subjects (mathematics, physics, chemistry, biology, history, law, medicine, engineering, philosophy, etc.) using 15,908 curated multiple-choice questions. The dataset stratifies questions by difficulty level from elementary to professional certification exams, enabling fine-grained assessment of model performance across knowledge domains and cognitive complexity tiers. Scoring is deterministic (exact match on selected choice) and comparable across models.","intents":["Compare language models on standardized knowledge benchmarks to rank frontier models objectively","Identify knowledge gaps and weak domains in a specific LLM before deployment","Track model improvement over training iterations or fine-tuning experiments","Validate that domain-specific training (medical, legal) actually improves performance on professional exams"],"best_for":["ML researchers and model developers benchmarking LLM capabilities","Organizations evaluating commercial LLMs for knowledge-intensive applications","Teams building domain-specific LLMs who need standardized evaluation"],"limitations":["Multiple-choice format doesn't measure reasoning depth or ability to generate novel solutions — only recognition and selection","No evaluation of explanation quality or reasoning chains; a model can guess correctly without understanding","Subject distribution is imbalanced (e.g., more STEM than humanities questions), skewing aggregate scores","Static snapshot of knowledge as of dataset creation date; doesn't measure ability to learn or update knowledge","English-only; no multilingual evaluation despite many LLMs supporting 100+ languages"],"requires":["Hugging Face datasets library (datasets>=2.0.0) or direct JSON/CSV parsing capability","LLM with multiple-choice question answering capability (any model with text generation)","Computational resources to run inference on 15,908 questions (typically 1-24 hours depending on model size and hardware)","Ability to parse and evaluate structured multiple-choice responses (A/B/C/D selection)"],"input_types":["question text (string)","four multiple-choice options (strings)","subject category (string)","difficulty level metadata (string)"],"output_types":["model prediction (single character: A/B/C/D)","accuracy score per subject (float 0-1)","aggregate accuracy across all subjects (float 0-1)","per-difficulty-level performance breakdown (structured data)"],"categories":["data-processing-analysis","model-evaluation","benchmark"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmlu-massive-multitask-language-understanding__cap_1","uri":"capability://data.processing.analysis.difficulty.stratified.performance.analysis","name":"difficulty-stratified performance analysis","description":"Segments the 15,908 questions into difficulty tiers (elementary, high school, college, professional) enabling builders to measure whether a model's knowledge is shallow pattern-matching or deep understanding. Each question is tagged with difficulty metadata, allowing disaggregated scoring that reveals performance cliffs — e.g., a model may score 85% on high school questions but only 40% on professional-level law or medicine questions. This stratification exposes whether improvements are broad-based or concentrated in easier domains.","intents":["Identify at what difficulty threshold a model's performance degrades significantly","Determine if a model is suitable for professional-grade applications (law, medicine) vs general knowledge tasks","Measure whether fine-tuning or RLHF actually improves reasoning on hard questions or just memorizes easy ones","Debug model weaknesses by isolating performance on specific difficulty bands"],"best_for":["Model developers optimizing for professional-grade applications","Teams evaluating whether an LLM is production-ready for high-stakes domains","Researchers studying scaling laws and whether model size correlates with reasoning depth"],"limitations":["Difficulty labels are subjective and assigned by dataset creators; no consensus on what 'professional' means across domains","Difficulty stratification doesn't measure reasoning transparency — a model might get hard questions right by luck or memorization","No per-question explanation of why a model failed, only binary correct/incorrect","Difficulty distribution is uneven across subjects (some subjects have more professional-level questions than others)"],"requires":["Ability to parse and filter questions by difficulty metadata field","Aggregation logic to compute per-difficulty-tier accuracy metrics","Sufficient inference budget to run full dataset (some models may be cost-prohibitive to evaluate on all 15,908 questions)"],"input_types":["question with difficulty label (string + metadata)","model predictions across all difficulty tiers"],"output_types":["accuracy breakdown by difficulty tier (dict: {elementary: 0.92, high_school: 0.85, college: 0.72, professional: 0.48})","performance cliff detection (boolean: does accuracy drop >20% between tiers?)","per-subject difficulty curves (structured data)"],"categories":["data-processing-analysis","model-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmlu-massive-multitask-language-understanding__cap_2","uri":"capability://data.processing.analysis.subject.specific.knowledge.profiling","name":"subject-specific knowledge profiling","description":"Organizes 15,908 questions into 57 distinct subject categories (mathematics, physics, chemistry, biology, history, law, medicine, engineering, philosophy, economics, etc.), enabling builders to generate per-subject accuracy profiles. Each question is tagged with its subject, allowing disaggregated scoring that reveals domain-specific strengths and weaknesses. A model might score 90% on STEM subjects but only 60% on humanities, or vice versa. This enables targeted evaluation for domain-specific applications.","intents":["Identify which academic domains a model excels or struggles in before deploying for domain-specific tasks","Measure whether domain-specific fine-tuning (e.g., medical LLM training) actually improves performance on professional exams","Compare models on specific subjects relevant to your use case rather than aggregate score","Detect biases or gaps in training data by examining subject-level performance patterns"],"best_for":["Organizations building domain-specific LLMs (medical, legal, financial) who need targeted evaluation","Researchers studying how training data composition affects knowledge distribution","Teams selecting LLMs for specialized applications where only certain subjects matter"],"limitations":["Subject categories are fixed at dataset creation; no ability to add custom domains or sub-specialties","Some subjects have fewer questions than others (imbalanced), making per-subject scores less reliable for low-sample subjects","Subject-level performance doesn't measure cross-domain reasoning or transfer learning","No ability to weight subjects differently based on application importance"],"requires":["Ability to parse and filter questions by subject metadata field","Aggregation logic to compute per-subject accuracy metrics","Visualization or reporting tool to display 57-subject performance matrix"],"input_types":["question with subject label (string + metadata)","model predictions across all subjects"],"output_types":["accuracy breakdown by subject (dict with 57 keys, each mapping to float 0-1)","subject ranking by model performance (sorted list)","subject-level confusion matrix or error analysis (structured data)","heatmap or matrix visualization (optional)"],"categories":["data-processing-analysis","model-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmlu-massive-multitask-language-understanding__cap_3","uri":"capability://data.processing.analysis.standardized.model.comparison.and.ranking","name":"standardized model comparison and ranking","description":"Provides a canonical, widely-adopted benchmark for comparing LLM capabilities across the industry. MMLU is the single most reported metric in LLM research papers and model cards, enabling builders to position their models against published baselines (GPT-4, Claude, Llama, etc.). Scoring is deterministic and reproducible: exact match on multiple-choice selection. The dataset is fixed and versioned, ensuring that comparisons across papers and time periods are valid. Leaderboards and published results enable quick competitive analysis.","intents":["Benchmark a new LLM against published baselines to understand its relative capability tier","Track model improvement over training iterations using a standardized, reproducible metric","Publish model results in a format that the research community recognizes and trusts","Make go/no-go decisions on model deployment based on published MMLU thresholds (e.g., 'production-ready if >80%')"],"best_for":["ML researchers and model developers publishing new LLMs","Organizations evaluating commercial LLMs and comparing published benchmarks","Teams making model selection decisions based on industry-standard metrics"],"limitations":["MMLU is a multiple-choice benchmark; doesn't measure open-ended reasoning, code generation, or creative tasks","Widespread adoption means the dataset may be partially memorized by newer models trained on internet data, inflating scores","No evaluation of reasoning transparency, explanation quality, or ability to justify answers","Aggregate score can mask significant domain-specific weaknesses (e.g., 75% overall but 40% on law)","Static benchmark doesn't adapt to model capabilities; no dynamic difficulty adjustment"],"requires":["Access to published MMLU results and leaderboards (Hugging Face, OpenAI, Anthropic, etc.)","Ability to run inference on the full 15,908-question dataset for fair comparison","Standardized evaluation script to ensure scoring methodology matches published results"],"input_types":["model predictions (single character per question: A/B/C/D)","published baseline results (float 0-1 accuracy)"],"output_types":["aggregate accuracy score (float 0-1)","percentile ranking vs published models (e.g., 'top 5% of open-source models')","comparison table vs baselines (structured data)","improvement delta vs previous model version (float)"],"categories":["data-processing-analysis","model-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmlu-massive-multitask-language-understanding__cap_4","uri":"capability://data.processing.analysis.reproducible.evaluation.with.fixed.question.set","name":"reproducible evaluation with fixed question set","description":"Provides a fixed, versioned dataset of 15,908 questions that doesn't change between evaluation runs, enabling reproducible and comparable results across different models, teams, and time periods. The dataset is immutable and publicly available on Hugging Face, ensuring that any builder can download the exact same questions and verify published results. This eliminates variance from question generation, sampling, or dataset drift that would occur with dynamic benchmarks.","intents":["Verify published model results by running evaluation on the exact same question set","Ensure that performance improvements are real and not artifacts of different evaluation datasets","Compare models trained at different times using a stable benchmark","Build reproducible evaluation pipelines that produce consistent results across runs and environments"],"best_for":["Researchers validating published claims or reproducing results","Teams building evaluation infrastructure that requires deterministic, repeatable benchmarks","Organizations comparing models across different time periods or training runs"],"limitations":["Fixed dataset means no adaptation to model capabilities or emerging knowledge; benchmark becomes stale over time","Immutability prevents fixing errors or biases discovered in questions after publication","Dataset size is fixed at 15,908 questions; no ability to add new questions or expand coverage","No versioning mechanism for question corrections or clarifications"],"requires":["Hugging Face datasets library or ability to download and cache the dataset locally","Deterministic evaluation script that produces identical results across runs (no randomness in question selection or scoring)","Version control or documentation of which MMLU version was used (original vs updated versions)"],"input_types":["model predictions (single character per question: A/B/C/D)","question set version identifier (string)"],"output_types":["exact accuracy score (float 0-1)","per-question result log (structured data: question_id, prediction, ground_truth, correct/incorrect)","reproducibility metadata (dataset version, evaluation timestamp, model version)"],"categories":["data-processing-analysis","model-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmlu-massive-multitask-language-understanding__cap_5","uri":"capability://data.processing.analysis.professional.certification.exam.alignment","name":"professional certification exam alignment","description":"Includes questions sourced from or aligned with real professional certification exams (law bar exams, medical licensing exams, engineering professional exams, etc.), enabling evaluation of whether LLMs can perform at professional-grade levels. Questions are tagged with difficulty levels that correspond to actual exam difficulty, and some questions are directly sourced from published exam materials. This grounds the benchmark in real-world professional standards rather than synthetic or academic-only questions.","intents":["Evaluate whether an LLM is capable of passing professional certification exams (law, medicine, engineering)","Assess readiness for deployment in high-stakes professional applications","Measure whether fine-tuning on professional domain data improves performance on actual certification exams","Benchmark against human performance on the same exams to understand model capability tier"],"best_for":["Organizations building LLMs for professional applications (legal AI, medical AI, etc.)","Researchers studying whether LLMs can achieve professional-grade competency","Teams evaluating LLMs for high-stakes use cases where certification-level performance is required"],"limitations":["Professional exam questions are a subset of MMLU, not the entire dataset; most questions are academic rather than professional-level","Passing a benchmark doesn't guarantee ability to pass actual certification exams (different format, time pressure, explanation requirements)","No evaluation of practical skills, ethics, or judgment required in professional practice — only knowledge","Professional exam alignment varies by subject; some subjects have more real exam questions than others","No measurement of whether model can explain reasoning or justify answers as required in professional practice"],"requires":["Ability to filter questions by difficulty level (professional tier)","Knowledge of which subjects have professional exam alignment","Comparison data on human performance on the same exams (for context)"],"input_types":["question with professional exam source metadata (string + metadata)","model predictions on professional-level questions"],"output_types":["professional-level accuracy score (float 0-1)","per-subject professional exam performance (dict)","comparison vs human performance on same exams (structured data)","certification readiness assessment (boolean or confidence score)"],"categories":["data-processing-analysis","model-evaluation"],"confidence":0.5,"matches":0,"success_rate":0},{"id":"mmlu-massive-multitask-language-understanding__headline","uri":"capability://testing.quality.standard.benchmark.for.evaluating.language.model.knowledge.and.reasoning","name":"standard benchmark for evaluating language model knowledge and reasoning","description":"The MMLU benchmark is the go-to standard for assessing the knowledge and reasoning capabilities of language models across a wide range of academic subjects, making it essential for developers and researchers looking to compare model performance.","intents":["best language model benchmark","benchmark for evaluating LLMs","MMLU for academic subject testing","language model comparison tool","standard tests for AI knowledge assessment"],"best_for":["evaluating LLMs","comparing model performance"],"limitations":[],"requires":[],"input_types":[],"output_types":[],"categories":["testing-quality"],"confidence":0.5,"matches":0,"success_rate":0}],"trust":{"score":61,"verified":false,"data_access_risk":"low","permissions":["Hugging Face datasets library (datasets>=2.0.0) or direct JSON/CSV parsing capability","LLM with multiple-choice question answering capability (any model with text generation)","Computational resources to run inference on 15,908 questions (typically 1-24 hours depending on model size and hardware)","Ability to parse and evaluate structured multiple-choice responses (A/B/C/D selection)","Ability to parse and filter questions by difficulty metadata field","Aggregation logic to compute per-difficulty-tier accuracy metrics","Sufficient inference budget to run full dataset (some models may be cost-prohibitive to evaluate on all 15,908 questions)","Ability to parse and filter questions by subject metadata field","Aggregation logic to compute per-subject accuracy metrics","Visualization or reporting tool to display 57-subject performance matrix"],"failure_modes":["Multiple-choice format doesn't measure reasoning depth or ability to generate novel solutions — only recognition and selection","No evaluation of explanation quality or reasoning chains; a model can guess correctly without understanding","Subject distribution is imbalanced (e.g., more STEM than humanities questions), skewing aggregate scores","Static snapshot of knowledge as of dataset creation date; doesn't measure ability to learn or update knowledge","English-only; no multilingual evaluation despite many LLMs supporting 100+ languages","Difficulty labels are subjective and assigned by dataset creators; no consensus on what 'professional' means across domains","Difficulty stratification doesn't measure reasoning transparency — a model might get hard questions right by luck or memorization","No per-question explanation of why a model failed, only binary correct/incorrect","Difficulty distribution is uneven across subjects (some subjects have more professional-level questions than others)","Subject categories are fixed at dataset creation; no ability to add custom domains or sub-specialties","builder identity is not verified yet","no observed match outcomes yet"],"rank_breakdown":{"adoption":0.7,"quality":0.8500000000000001,"ecosystem":0.3,"match_graph":0.25,"freshness":0.75,"weights":{"adoption":0.25,"quality":0.35,"ecosystem":0.15,"match_graph":0.2,"freshness":0.05}},"observed_outcomes":{"matches":0,"success_rate":0,"avg_confidence":0,"top_intents":[],"last_matched_at":null},"maintenance":{"status":"active","updated_at":"2026-05-24T12:16:23.328Z","last_scraped_at":null,"last_commit":null},"community":{"stars":null,"forks":null,"weekly_downloads":null,"model_downloads":null,"model_likes":null}},"distribution":{"claim_url":"https://unfragile.ai/submit?claim=mmlu-massive-multitask-language-understanding","compare_url":"https://unfragile.ai/compare?artifact=mmlu-massive-multitask-language-understanding"}},"signature":"nfBRGkGPH9XeEWvAoLcy1/+kKSptZhdsRFYjONgh6LjdjMlYHRTiTiAcQtVoiEKn9RfVtp7FPNsxyM82aToUBQ==","signedAt":"2026-06-21T07:49:39.966Z","signedBy":"unfragile.ai","version":1},"_links":{"self":"https://unfragile.ai/api/v1/passport/mmlu-massive-multitask-language-understanding","artifact":"https://unfragile.ai/mmlu-massive-multitask-language-understanding","verify":"https://unfragile.ai/api/v1/verify?slug=mmlu-massive-multitask-language-understanding","publicKey":"https://unfragile.ai/api/v1/trust-passport-public-key","spec":"https://unfragile.ai/trust","schema":"https://unfragile.ai/schema.json","docs":"https://unfragile.ai/docs"}}