mlsanityData to 14 Oct 2026

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

arXiv 2505.07891 ↗First posted 11 May 2025cs.CL cs.AIcited by 4

On the map: Retrieval-augmented generation → Medical and biomedical RAG

Abstract (arXiv)

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.

Key facts. Cited by 4 papers in the map (data through 14 Oct 2026). Placed in Medical and biomedical RAG, one of 15 directions in Retrieval-augmented generation. Builds on 22 of its 58 references that are in the map. First posted on arXiv 11 May 2025.

Builds on

22 of 58 references.

References · 22

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10 more
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Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 3.

4 papers.

  1. 2025A Query-Aware Multi-Path Knowledge Graph Fusion Approach for Enhancing Retrieval-Augmented Generation in Large Language Modelscited by 1
  2. 2026LLM-Enhanced Rumor Detection via Virtual Node Induced Edge Predictioncited by 1
  3. 2026AsynDBT: Asynchronous Distributed Bilevel Tuning for efficient In-Context Learning with Large Language Modelscited by 0
  4. 2026An Evidence-Grounded Retrieval-Augmented Transformer Framework for Health Misinformation Verificationcited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Enhancing Health Fact-Checking with LLM-Generated Synthetic Data
  2. 2025GraphCheck: Breaking Long-Term Text Barriers with Extracted Knowledge Graph-Powered Fact-Checking
  3. 2025FActBench: A Benchmark for Fine-grained Automatic Evaluation of LLM-Generated Text in the Medical Domain
  4. 2024Heterogeneous Graph Reasoning for Fact Checking over Texts and Tables
  5. 2025Hybrid Fact-Checking that Integrates Knowledge Graphs, Large Language Models, and Search-Based Retrieval Agents Improves Interpretable Claim Verification
  6. 2025FactCG: Enhancing Fact Checkers with Graph-Based Multi-Hop Data
  7. 2025Combating Biomedical Misinformation through Multi-modal Claim Detection and Evidence-based Verification
  8. 2026Beyond Verdicts: A Graph-Based Analysis of Human and LLM Reasoning in Scientific Fact-Checking