metadata

annotations_creators:
  - expert-generated
language:
  - en
license: mit
multilinguality:
  - monolingual
size_categories:
  - 10K<n<100K
task_categories:
  - multiple-choice
task_ids:
  - multiple-choice-qa
pretty_name: MNLP M3 MCQA Dataset

MNLP M3 MCQA Dataset

The MNLP M3 MCQA Dataset is a carefully curated collection of Multiple-Choice Question Answering (MCQA) examples, unified from several academic and benchmark datasets.

Developed as part of the CS-552: Modern NLP course at EPFL (Spring 2025), this dataset is designed for training and evaluating models on multiple-choice QA tasks, particularly in the STEM and general knowledge domains.

Key Features

~30,000 MCQA questions
6 diverse sources: SciQ, OpenBookQA, MathQA, ARC-Easy, ARC-Challenge, and MedMCQA
Each question has exactly 4 options (A–D) and one correct answer
Covers a wide range of topics: science, technology, engineering, mathematics, and general knowledge

Dataset Structure

Each example is a dictionary with the following fields:

Field	Type	Description
`dataset`	`string`	Source dataset (`sciq`, `openbookqa`, etc.)
`id`	`string`	Unique identifier for the question
`question`	`string`	The question text
`choices`	`list`	List of 4 answer options (corresponding to A–D)
`answer`	`string`	The correct option, as a letter: `"A"`, `"B"`, `"C"`, or `"D"`
`support`	`string`	A brief explanation or fact supporting the correct answer when available

Example:
```json
{
  "dataset": "sciq",
  "id": "sciq_01_00042",
  "question": "What does a seismograph measure?",
  "choices": ["Earthquakes", "Rainfall", "Sunlight", "Temperature"],
  "answer": "A",
  "support": "A seismograph is an instrument that detects and records earthquakes."
}

Source Datasets

This dataset combines multiple high-quality MCQA sources to support research and fine-tuning in STEM education and reasoning. The full corpus contains 29,870 multiple-choice questions from the following sources:

Source (Hugging Face)	Name	Size	Description & Role in the Dataset
`allenai/sciq`	SciQ	11,679	Science questions (Physics, Chemistry, Biology, Earth science). Crowdsourced with 4 answer choices and optional supporting evidence. Used to provide well-balanced, factual STEM questions at a middle/high-school level.
`allenai/openbookqa`	OpenBookQA	4,957	Science exam-style questions requiring multi-step reasoning and use of commonsense or external knowledge. Contributes more challenging and inference-based questions.
`allenai/math_qa`	MathQA	5,000	Subsample of quantitative math word problems derived from AQuA-RAT, annotated with structured answer options. Introduces numerical reasoning and problem-solving components into the dataset.
`allenai/ai2_arc` (config: `ARC-Easy`)	ARC-Easy	2,140	Science questions at the middle school level. Useful for testing basic STEM understanding and factual recall. Filtered to retain only valid 4-choice entries.
`allenai/ai2_arc` (config: `ARC-Challenge`)	ARC-Challenge	1,094	More difficult science questions requiring reasoning and inference. Widely used as a benchmark for evaluating LLMs. Also filtered for clean MCQA format compatibility.
`openlifescienceai/medmcqa`	MedMCQA	5,000	A subsample of multiple-choice questions on medical topics from various exams, filtered for a single-choice format. Contains real-world and domain-specific clinical reasoning questions covering various medical disciplines.

Intended Applications and Structure

This dataset is split into three parts:

train (~85%) — for training MCQA models
validation (~15%) — for tuning and monitoring performance during training

It is suitable for multiple-choice question answering tasks, especially in the STEM domain (Science, Technology, Engineering, Mathematics).

Author

This dataset was created and published by Youssef Belghmi as part of the CS-552: Modern NLP course at EPFL (Spring 2025).