README.md

metadata

dataset_info:
  features:
    - name: id
      dtype: string
    - name: dataset
      dtype: string
    - name: question
      dtype: string
    - name: options
      sequence: string
    - name: answer
      dtype: string
  splits:
    - name: train
      num_bytes: 2268293
      num_examples: 10687
  download_size: 1254741
  dataset_size: 2268293
configs:
  - config_name: default
    data_files:
      - split: train
        path: data/train-*

This dataset was constructed as part of the EPFL Modern NLP (MNLP) course project to train and evaluate large language models on multiple-choice question answering (MCQA) tasks focused on scientific reasoning.

It aggregates and reformats 10,687 unique examples from five high-quality academic and biomedical QA datasets, applying consistent structure, question normalization, and cross-source deduplication.

📊 Dataset Composition

Source Dataset	Link	Questions Used	Description
ARC-Challenge	ai2_arc	1,119	Harder science exam questions requiring multi-step reasoning
ARC-Easy	ai2_arc	2,251	Simpler science questions at the elementary/middle school level
QASC	qasc	3,000 (subset)	A filtered and deduplicated subset of the QASC dataset, which was originally larger (~8,000+ examples). Only 3,000 unique and diverse questions were selected for balance
OpenBookQA	openbookqa	3,317	4-option science questions, filtered to keep humanScore ≥ 1
PubMedQA	pubmed_qa	1,000	Biomedical questions with Yes/No/Maybe answers based on PubMed abstracts

🧪 Preprocessing Pipeline

• Normalization: All questions were lowercased and stripped of whitespace for consistency.
• Deduplication: Each question was hashed (md5(lowercase question)) to detect and eliminate duplicates across datasets.
• Filtering:
  • OpenBookQA was filtered to retain only questions with humanScore ≥ 1.
  • PubMedQA was filtered to retain only labeled questions with answers in {yes, no, maybe}.
  • QASC was sampled and capped at 3,000 unique questions to ensure dataset balance.
• Unified formatting: All entries follow the same JSON schema across sources.

📦 Format

Each sample follows this structure:

{
"id": "qasc_481",
"dataset": "qasc",
"question": "What do bees use to make honey?",
"options": ["nectar", "pollen", "water", "leaves"],
"answer": "A"
}