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

By effectively merging these two retrieval paradigms, the system would be capable of assembling a more comprehensive evidence base, thereby reducing the likelihood of missing pertinent details. Additionally, incorporating incremental graph update techniques would enable TrumorGPT to seamlessly integrate new medical studies and real-time health news without the need for extensive re-indexing or system downtime. This continuous update process is particularly crucial in the dynamic field of health, where the rapid emergence of new data can significantly impact the accuracy of fact-checking outcomes. In addition to efficient updating, implementing a dual-level retrieval strategy can further enhance contextual reasoning. Under this strategy, an initial coarse-grained retrieval would rapidly identify broad thematic and relational contexts, while a subsequent fine-grained search would extract specific factual details. This layered retrieval approach not only ensures that both high-level and granular information is captured but also supports more robust multi-hop reasoning by effectively bridging the gap between abstract concepts and concrete facts. Thus, these enhancements would bolster the fact-checking framework of TrumorGPT, striking an optimal balance between precision, efficiency, and comprehensive reasoning.