{
 "body": "\nPart of the [MMLU](mmlu.md) family.\n\n## What it measures\n\nUSMLE-style clinical vignettes: a patient history, examination and test findings, followed by a\nquestion on the most likely diagnosis, underlying mechanism or next step in management, spanning\nspecialties from obstetrics to neurology to pediatrics. Like the rest of MMLU, each question gives\nfour labelled options and the model is graded on picking the single correct one, typically\nevaluated 5-shot. The benchmark's own categorisation places this subject in the \"health\"\nsubcategory, within the \"other\" group of MMLU's four broad areas (STEM, humanities, social\nsciences, and other), alongside subjects such as anatomy, clinical knowledge and nutrition.\n\n## Reading the numbers\n\nThe Hugging Face mirror of this subject holds 272 test questions (used for scoring), plus 31\nvalidation and 5 dev questions for few-shot prompting. With well under a thousand items, a handful\nof questions can shift the reported percentage by a point or two, so treat small differences\nbetween models on this subject alone as noisy rather than meaningful. A high score means the model\nrecalls clinical patterns and guideline-consistent management well under exam phrasing; it is not\nevidence that the model can safely manage an actual patient. Read it against a model's overall\nMMLU score and against other subjects in the \"other\" group rather than in isolation, and see the\n[MMLU](mmlu.md) family page for the shared scoring protocol, saturation and contamination notes\nthat apply here too.\n",
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 ],
 "page": {
  "category": "knowledge",
  "dataset": {
   "languages": [
    "en"
   ],
   "license": "MIT",
   "modalities": [
    "text"
   ],
   "public_test_set": true,
   "size": 272,
   "size_note": "272 test questions (used for scoring), plus 31 validation and 5 dev (few-shot prompt) questions, per the Hugging Face parquet mirror of cais/mmlu, config 'professional_medicine'.",
   "splits": "dev (5), validation (31), test (272)",
   "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_medicine",
   "lm_eval": "mmlu_professional_medicine",
   "other": "hendrycksTest-professional_medicine in the pre-2024 Open LLM Leaderboard v1 harness fork"
  },
  "id": "mmlu_professional_medicine",
  "leaderboard_url": "https://github.com/hendrycks/test",
  "lineage": {
   "family": "mmlu"
  },
  "measures": "USMLE-style clinical vignettes: a patient history and findings followed by a question on diagnosis, mechanism or next step in management. Questions are four-option multiple-choice, drawn from the MMLU test set's \"health\" subcategory within the benchmark's \"other\" 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 Medicine",
  "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_medicine task config",
    "url": "https://github.com/EleutherAI/lm-evaluation-harness/blob/main/lm_eval/tasks/mmlu/default/mmlu_professional_medicine.yaml"
   }
  ],
  "status": "active",
  "subcategory": "health",
  "summary": "MMLU subject subset: USMLE-style clinical vignettes on diagnosis, mechanism and management across medical specialties.",
  "tags": [
   "knowledge",
   "multiple-choice",
   "mmlu-subset",
   "other"
  ],
  "task_format": "Four-option multiple-choice questions, graded on the single correct labelled option; commonly evaluated 5-shot, consistent with the rest of MMLU."
 }
}