metadata
license: agpl-3.0
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: val
path: data/val-*
- split: test
path: data/test-*
dataset_info:
features:
- name: input
dtype: string
splits:
- name: train
num_bytes: 136602851.95652175
num_examples: 7260
- name: val
num_bytes: 17065948.584650856
num_examples: 907
- name: test
num_bytes: 17084764.40447958
num_examples: 908
download_size: 82888007
dataset_size: 170753564.9456522
task_categories:
- text-generation
- question-answering
- summarization
language:
- en
tags:
- biology
- biomedicine
pretty_name: PubMed Referenced Question Answering Dataset
size_categories:
- 10M<n<100M
Dataset description
The PQAref dataset is a dataset for fine-tuning large language models for referenced question-answering in biomedical domain.
The dataset contains 3 components:
- Instruction - question that is supposed to be answered
- Abstracts - set of 10 relevant abstracts retrieved from PubMed by an IR system. They contain the PubMed id, abstract title and the content of the abstract
- Answer - expected answer, with references in the form of PubMed IDs.
The dataset was created semi-automatically, utilizing questions available from PubMedQA dataset.
Paper
Bojana Bašaragin, Adela Ljajić, Darija Medvecki, Lorenzo Cassano, Miloš Košprdić, and Nikola Milošević. 2024. How do you know that? Teaching Generative Language Models to Reference Answers to Biomedical Questions. In Proceedings of the 23rd Workshop on Biomedical Natural Language Processing, pages 536–547, Bangkok, Thailand. Association for Computational Linguistics, DOI: 10.18653/v1/2024.bionlp-1.44, URL: https://aclanthology.org/2024.bionlp-1.44