---
task_categories:
- text-generation
---

# MedMCQA-50

This dataset expands
[`rohan2810/medmcqa`](https://huggingface.co/datasets/rohan2810/medmcqa)
from 20 to 50 candidates per example for finite-pool preference-optimization
experiments.

## Construction

For every example, all unique candidates in the original 20-entry pool are
preserved. Duplicate answer strings in the source are collapsed while retaining
their first occurrence. Additional distractors are sampled deterministically
from the 172,635-answer source candidate universe
using seed `1958` until each row has exactly 50 unique candidates.
The resulting candidates are deterministically shuffled, and the candidate list
embedded in `fixed_prompt` is replaced accordingly.

No model scores, embeddings, selector objectives, or evaluation outcomes are
used to construct the candidate pools. This nested construction limits
dataset-generation confounding when studying a larger candidate pool.

## Splits

- `train`: 98,864 rows
- `validation`: 12,358 rows
- `test`: 12,359 rows

Each row contains:

- `fixed_prompt`: the medical question and the same 50 answer candidates.
- `itemList`: 50 unique answer candidates.
- `trueSelection`: the true answer, appearing exactly once in `itemList`.

## Provenance

- Source repository: `rohan2810/medmcqa`
- Source revision: `b4944b5c7085a408c24f50f4b2ae8eed0ef66239`
- Output repository: `rohan2810/medmcqa50`
- Generator seed: `1958`
- Generator: `neurips_rebuttal/medmcqa50_dataset/build_medmcqa50.py`