|
|
--- |
|
|
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 |
