{
 "body": "\nPart of the [MMLU](mmlu.md) family.\n\n## What it measures\n\nBar-exam-style fact patterns testing US law: contracts, torts, criminal law, property and\nevidence, each followed by four labelled options that resolve to a single correct rule\napplication. Like the rest of MMLU, the model is graded on picking that one correct option,\ntypically evaluated 5-shot. The benchmark's own categorisation places this subject in the \"law\"\nsubcategory, within the \"humanities\" group of MMLU's four broad areas (STEM, humanities, social\nsciences, and other), alongside international law and jurisprudence.\n\n## Reading the numbers\n\nThe Hugging Face mirror holds 1,534 test questions (scored) plus 170 validation and 5 dev -- by\nfar the largest of MMLU's 57 subjects, almost 640 more than the next largest (moral scenarios, at\n895) and roughly fifteen times the smallest (100 each). That size makes single-question noise less\nof a concern here than on most MMLU subjects. A high score means the model applies legal rules\ncorrectly to fact patterns under exam phrasing; it says nothing about drafting a legal document or\nhandling a jurisdiction's actual case law. Read it against a model's overall MMLU score and other\nsubjects in the \"humanities\" group, and see the [MMLU](mmlu.md) family page for the shared scoring\nprotocol, saturation and contamination notes that apply here too.\n",
 "build": {
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  "commit": "0a599558854c0e238c03a0f0d725239cb28f9d11",
  "eligibility_as_of": "2026-09-09"
 },
 "disposition": {
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  "reasons": [],
  "status": "unassessed",
  "verified_results": []
 },
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 ],
 "page": {
  "category": "knowledge",
  "dataset": {
   "languages": [
    "en"
   ],
   "license": "MIT",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 1534,
   "size_note": "1,534 test questions (used for scoring), plus 170 validation and 5 dev (few-shot prompt) questions, per the Hugging Face parquet mirror of cais/mmlu, config 'professional_law'. By far the largest of MMLU's 57 subject test splits -- the next largest is moral_scenarios at 895 -- and about fifteen times the size of the smallest subjects (100 questions each).",
   "splits": "dev (5), validation (170), test (1,534)",
   "url": "https://huggingface.co/datasets/cais/mmlu"
  },
  "freshness": {
   "researched": "2026-09-08",
   "researched_by": "sonnet-5 agent, batch 1b, slice N"
  },
  "harness": {
   "helm": "mmlu:subject=professional_law",
   "lm_eval": "mmlu_professional_law",
   "other": "hendrycksTest-professional_law in the pre-2024 Open LLM Leaderboard v1 harness fork"
  },
  "id": "mmlu_professional_law",
  "leaderboard_url": "https://github.com/hendrycks/test",
  "lineage": {
   "family": "mmlu"
  },
  "measures": "Bar-exam-style fact patterns testing US law across contracts, torts, criminal law, property and evidence, each resolved to a single correct rule application. Questions are four-option multiple-choice, drawn from the MMLU test set's \"law\" subcategory within the benchmark's \"humanities\" top-level group, and are graded on the single correct labelled option.",
  "metric": {
   "baseline_note": "25% is the four-option random-guess rate. No subject-specific human baseline is given by the paper for this subject; see the mmlu family page for the benchmark-wide human baselines.",
   "direction": "higher_is_better",
   "max_score": 100,
   "name": "accuracy",
   "random_baseline": 25,
   "unit": "%"
  },
  "name": "MMLU: Professional Law",
  "page_kind": "subset",
  "paper": {
   "arxiv": "2009.03300",
   "title": "Measuring Massive Multitask Language Understanding",
   "url": "https://arxiv.org/abs/2009.03300",
   "year": 2021
  },
  "publisher": {
   "authors": [
    "Dan Hendrycks",
    "Collin Burns",
    "Steven Basart",
    "Andy Zou",
    "Mantas Mazeika",
    "Dawn Song",
    "Jacob Steinhardt"
   ],
   "org": "UC Berkeley (original); Center for AI Safety (current host)",
   "url": "https://github.com/hendrycks/test"
  },
  "released": "2020-09",
  "repo_url": "https://github.com/hendrycks/test",
  "sources": [
   {
    "accessed": "2026-09-08",
    "title": "Measuring Massive Multitask Language Understanding (Hendrycks et al., arXiv:2009.03300)",
    "url": "https://arxiv.org/abs/2009.03300"
   },
   {
    "accessed": "2026-09-08",
    "title": "hendrycks/test GitHub repository (MMLU reference implementation)",
    "url": "https://github.com/hendrycks/test"
   },
   {
    "accessed": "2026-09-08",
    "title": "cais/mmlu dataset card, Hugging Face",
    "url": "https://huggingface.co/datasets/cais/mmlu"
   },
   {
    "accessed": "2026-09-08",
    "title": "cais/mmlu datasets-server size API (per-subject row counts)",
    "url": "https://datasets-server.huggingface.co/size?dataset=cais/mmlu"
   },
   {
    "accessed": "2026-09-08",
    "title": "lm-evaluation-harness mmlu_professional_law task config",
    "url": "https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/mmlu/default/mmlu_professional_law.yaml"
   }
  ],
  "status": "active",
  "subcategory": "law",
  "summary": "MMLU subject subset: Bar-exam-style fact patterns testing US law, and by far the largest of MMLU's 57 subjects.",
  "tags": [
   "knowledge",
   "multiple-choice",
   "mmlu-subset",
   "humanities"
  ],
  "task_format": "Four-option multiple-choice questions, graded on the single correct labelled option; commonly evaluated 5-shot, consistent with the rest of MMLU."
 }
}