TrumorGPT：基于图的检索增强大语言模型用于事实核查
TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking
AI总结：
TrumorGPT利用大语言模型和基于图的检索增强生成技术，构建语义健康知识图谱，实现健康领域谣言的事实核查，有效区分真实与虚假信息。
摘要：
在社交媒体时代，错误信息和谣言迅速传播，导致了信息疫情的出现，虚假信息对社会构成重大威胁。为了解决这一问题，我们引入了TrumorGPT，一种新颖的生成式人工智能解决方案，专为健康领域的事实核查而设计。TrumorGPT旨在区分“trumors”，即被证实为真实的健康相关谣言，为区分单纯猜测与经过验证的事实提供关键工具。该框架利用具有少样本学习能力的大语言模型（LLM）进行语义健康知识图谱构建和语义推理。TrumorGPT结合了基于图的检索增强生成（GraphRAG）技术，以解决LLM中常见的幻觉问题和静态训练数据的局限性。GraphRAG涉及访问和利用定期更新的语义健康知识图谱中的信息，这些图谱包含最新的医学新闻和健康信息，确保TrumorGPT的事实核查基于最新数据。通过使用广泛的医疗数据集进行评估，TrumorGPT在公共卫生声明的事实核查中展现出优越性能。其跨平台有效进行事实核查的能力标志着在对抗健康相关错误信息方面迈出了关键一步，增强了数字信息时代的信任和准确性。
英文摘要：
In the age of social media, the rapid spread of misinformation and rumors has led to the emergence of infodemics, where false information poses a significant threat to society. To combat this issue, we introduce TrumorGPT, a novel generative artificial intelligence solution designed for fact-checking in the health domain. TrumorGPT aims to distinguish "trumors", which are health-related rumors that turn out to be true, providing a crucial tool in differentiating between mere speculation and verified facts. This framework leverages a large language model (LLM) with few-shot learning for semantic health knowledge graph construction and semantic reasoning. TrumorGPT incorporates graph-based retrieval-augmented generation (GraphRAG) to address the hallucination issue common in LLMs and the limitations of static training data. GraphRAG involves accessing and utilizing information from regularly updated semantic health knowledge graphs that consist of the latest medical news and health information, ensuring that fact-checking by TrumorGPT is based on the most recent data. Evaluating with extensive healthcare datasets, TrumorGPT demonstrates superior performance in fact-checking for public health claims. Its ability to effectively conduct fact-checking across various platforms marks a critical step forward in the fight against health-related misinformation, enhancing trust and accuracy in the digital information age.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
