VaxGuard

Name: VaxGuard
Creator: Macquarie University, Australia; University of Sydney, Australia

VaxGuard是一个由LLM生成的疫苗相关错误信息数据集，包含12万条样本，覆盖多种疫苗类型和角色，用于检测错误信息。

Macquarie University, Australia; University of Sydney, Australia

arXiv

2025-03-12 更新

文本cs.CL疫苗编码

基本信息

模态

文本

创建/更新时间

2025-03-12

资源简介

VaxGuard

【数据集背景】

本数据集源站标题为《arXiv Query: search_query=&id_list=2503.09103v1&start=0&max_results=1》。源站首次发布：2025-03-12。

【数据内容】

源站原始描述：Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities. However, they also present challenges, particularly in generating vaccine-related misinformation, which poses risks to public health. Despite research on human-authored misinformation, a notable gap remains in understanding how LLMs contribute to vaccine misinformation and how best to detect it. Existing benchmarks often overlook vaccine-specific misinformation and the diverse roles of m…

【主题与分类】

主题标签：cs.CL。

【适用方向】

文本类数据可用于医学自然语言处理，如命名实体识别、信息抽取与文本分类。

【获取与许可】

源站页面：https://arxiv.org/abs/2503.09103v1

原始链接

http://arxiv.org/abs/2503.09103v1

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使用方式

数据集说明

VaxGuard 对应论文数据集（arXiv 预印本）。

数据获取指引

打开论文页面获取作者与项目信息：https://arxiv.org/abs/2503.09103v1
论文 Data Availability / Code Availability 章节标注了数据实际托管位置；
获取到实际数据链接后，按对应平台标准方式下载。

论文摘要：Abstract:Recent advancements in Large Language Models (LLMs) have significantly improved text generation capabilities. However, they also present challenges, particularly in generating vaccine-related misinformation, which poses risks to public health. Despite research on human-authored misinformation, a notable gap remains in understanding how LLMs contribute to vaccine misinformation and how best to detect it. Existing benchmarks often overlook vaccine-specific misinformation and the diverse roles of misinformation spreaders. This paper introduces VaxGuard, a novel dataset designed to address these challenges. VaxGuard includes vaccine-related misinformation generated by multiple LLMs and provides a comprehensive framework for detecting misinformation across various roles. Our findings show that GPT-3.5 and GPT-4o consistently outperform other LLMs in detecting misinformation, especially when dealing with subtle or emotionally charged narratives. On the other hand, PHI3 and Mistral show lower performance, struggling with precision and recall in fear-driven contexts. Additionally, detection performance tends to decline as input text length increases, indicating the need for improved methods to handle larger content. These results highlight the importance of role-specific detection strategies and suggest that VaxGuard can serve as a key resource for improving the detection of LLM-generated vaccine misinformation.