---
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.",
}
```