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---
dataset_info:
features:
- name: question
dtype: string
- name: background
dtype: string
- name: objective
dtype: string
- name: conclusion
dtype: string
- name: verdicts
dtype: string
- name: strength
dtype: string
- name: label
dtype: int64
- name: category
dtype: string
splits:
- name: train
num_bytes: 4679909
num_examples: 2786
download_size: 2365567
dataset_size: 4679909
language:
- en
tags:
- medical
size_categories:
- 1K<n<10K
task_categories:
- question-answering
- text-classification
---
# Dataset Card for "MedREQAL"
This dataset is the converted version of [MedREQAL](https://github.com/jvladika/MedREQAL).
## Reference
If you use MedREQAL, please cite the original paper:
```
@inproceedings{vladika-etal-2024-medreqal,
title = "{M}ed{REQAL}: Examining Medical Knowledge Recall of Large Language Models via Question Answering",
author = "Vladika, Juraj and
Schneider, Phillip and
Matthes, Florian",
editor = "Ku, Lun-Wei and
Martins, Andre and
Srikumar, Vivek",
booktitle = "Findings of the Association for Computational Linguistics ACL 2024",
month = aug,
year = "2024",
address = "Bangkok, Thailand and virtual meeting",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.findings-acl.860",
pages = "14459--14469",
abstract = "In recent years, Large Language Models (LLMs) have demonstrated an impressive ability to encode knowledge during pre-training on large text corpora. They can leverage this knowledge for downstream tasks like question answering (QA), even in complex areas involving health topics. Considering their high potential for facilitating clinical work in the future, understanding the quality of encoded medical knowledge and its recall in LLMs is an important step forward. In this study, we examine the capability of LLMs to exhibit medical knowledge recall by constructing a novel dataset derived from systematic reviews {--} studies synthesizing evidence-based answers for specific medical questions. Through experiments on the new MedREQAL dataset, comprising question-answer pairs extracted from rigorous systematic reviews, we assess six LLMs, such as GPT and Mixtral, analyzing their classification and generation performance. Our experimental insights into LLM performance on the novel biomedical QA dataset reveal the still challenging nature of this task.",
}
``` |