medmcqa-hard

A harder, de-duplicated remix of MedMCQA designed to reduce memorization and strengthen medical MCQ generalization.

Why “hard”?

• Answer list variants: Each correct option appears in multiple phrasing/list variants (e.g., reordered enumerations, equivalent wording), so models can’t rely on surface-form recall and must reason over content.
• RL-friendly targets: Every item includes one canonical correct answer and both single and set of incorrect answers → plug-and-play for DPO, RLAIF/GRPO, and contrastive objectives.
• Chat formatting: Adds lightweight messages (and optional system_prompt) not present in the original dataset, making it convenient for instruction-tuned models and SFT.

Intended uses

• Robust eval of medical QA beyond memorization.
• SFT with chat-style prompts.
• DPO / other RL setups using single_incorrect_answer or incorrect_answers.

Data schema (fields)

• question: str
• options: list[str] (usually 4)
• letter: str (A/B/C/D)
• cop: int (0-based index of correct option)
• incorrect_answers: list[str]
• single_incorrect_answer: str
• messages: list[{role: "system"|"user"|"assistant", content: str}]
• system_prompt: str (optional)

Example

{
  "question": "Which of the following is true about …?",
  "options": ["A …", "B …", "C …", "D …"],
  "letter": "C",
  "cop": 2,
  "incorrect_answers": ["A …", "B …", "D …"],
  "single_incorrect_answer": "B …",
  "messages": [
    {"role":"system","content":"You are a medical tutor."},
    {"role":"user","content":"Q: Which of the following…?\nA) …\nB) …\nC) …\nD) …"}
  ]
}

Source & attribution

Derived from MedMCQA (Pal, Umapathi, Sankarasubbu; CHIL 2022). Please cite the original dataset/paper when using this work.

Safety note: Research/education only. Not for clinical use.