TrumorGPT: Graph-Based Retrieval-Augmented Large Language Model for Fact-Checking

TL;DR

TrumorGPT is a graph-based retrieval-augmented language model designed to improve health-related fact-checking by leveraging up-to-date medical knowledge graphs and semantic reasoning, effectively combating misinformation.

Contribution

It introduces a novel graph-augmented framework that combines LLMs with dynamic health knowledge graphs for accurate fact-checking in the health domain.

Findings

Outperforms existing models in health claim verification

Effectively reduces hallucinations in LLM outputs

Utilizes latest medical data for real-time fact-checking

Abstract

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.