Datasets:
Tasks:
Multiple Choice
Modalities:
Text
Formats:
parquet
Sub-tasks:
multiple-choice-qa
Languages:
English
Size:
10K - 100K
License:
| 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 | | |
| ```markdown | |
| 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](https://huggingface.co/youssefbelghmi) as part of the *CS-552: Modern NLP* course at EPFL (Spring 2025). | |