医学领域问答数据集对比

数据集获取（Figshare）

• 源站标题：Comparison of medical domain QA datasets.
• DOI：10.1371/journal.pone.0333075.t001
• 发布日期：2025-10-09
• 文件数：1 个
• 文件总体积：5.63 KB
• 源站页面：https://plos.figshare.com/articles/dataset/Comparison_of_medical_domain_QA_datasets_/30321497

命令行下载

curl -L -o "Table 1.xls" "https://ndownloader.figshare.com/files/58607228"

文件清单

• Table 1.xls（5.63 KB）

文件总体积 5.63 KB（源站 API 实测）。

数据加载示例（图像类）

from PIL import Image
import glob, os

files = (glob.glob(os.path.join(path, "**", "*.png"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.jpg"), recursive=True)
       + glob.glob(os.path.join(path, "**", "*.tif"), recursive=True))
print("图像文件数:", len(files))
img = Image.open(files[0]); print("尺寸/模式:", img.size, img.mode)

# torchvision Dataset 方式:
# from torchvision import datasets
# ds = datasets.ImageFolder(path)  # 要求 子目录=类别

目录组织与标注格式以源站说明和下载后实际文件为准。

数据说明

While large language models (LLMs) have made significant advances in various fields, The study of applying LLMs to infectious disease-specific tasks has lagged behind. This study addresses this gap by introducing the Infectious Disease Question and Answering Dataset (IDQuAD), which is a novel dataset designed to train and evaluate LLMs in infectious disease-related queries. IDQuAD is constructed using medical papers, patents, and news, and employs innovative methodologies such as generating answers before questions and using counterfactual thinking to enhance the quality of the Question Answering (QA) pairs. In the experimental phase, we fine-tuned the Mistral-7B model on the IDQuAD dataset to test the effectiveness of our proposed datasets on LLM performance in QA tasks related to infectious diseases. The fine-tuned Mistral-7B model demonstrated substantial performance improvements, with its EM score increasing from 28.49% to 65.47% in the one-shot setting. Additionally, we evaluated other LLMs across various setups. Among all models tested, our fine-tuned model achieved the highest performance across metrics and settings. In conclusion, this study introduces IDQuAD as a foundational dataset for infectious disease research, demonstrating the effectiveness of fine-tuning LLMs and paving the way for future advances in dataset development and LLM refinement for infectious disease tasks.

许可

CC BY 4.0

引用

学术使用请引用 DOI 10.1371/journal.pone.0333075.t001（source: https://plos.figshare.com/articles/dataset/Comparison_of_medical_domain_QA_datasets_/30321497）。