Use of Retrieval-Augmented Large Language Model Agent for Long-Form COVID-19 Fact-Checking
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Abstract (arXiv)
The COVID-19 infodemic calls for scalable fact-checking solutions that handle long-form misinformation with accuracy and reliability. This study presents SAFE (system for accurate fact extraction and evaluation), an agent system that combines large language models with retrieval-augmented generation (RAG) to improve automated fact-checking of long-form COVID-19 misinformation. SAFE includes two agents - one for claim extraction and another for claim verification using LOTR-RAG, which leverages a 130,000-document COVID-19 research corpus. An enhanced variant, SAFE (LOTR-RAG + SRAG), incorporates Self-RAG to refine retrieval via query rewriting. We evaluated both systems on 50 fake news articles (2-17 pages) containing 246 annotated claims (M = 4.922, SD = 3.186), labeled as true (14.1%), partly true (14.4%), false (27.0%), partly false (2.2%), and misleading (21.0%) by public health professionals. SAFE systems significantly outperformed baseline LLMs in all metrics (p < 0.001). For consistency (0-1 scale), SAFE (LOTR-RAG) scored 0.629, exceeding both SAFE (+SRAG) (0.577) and the baseline (0.279). In subjective evaluations (0-4 Likert scale), SAFE (LOTR-RAG) also achieved the highest average ratings in usefulness (3.640), clearness (3.800), and authenticity (3.526). Adding SRAG slightly reduced overall performance, except for a minor gain in clearness. SAFE demonstrates robust improvements in long-form COVID-19 fact-checking by addressing LLM limitations in consistency and explainability. The core LOTR-RAG design proved more effective than its SRAG-augmented variant, offering a strong foundation for scalable misinformation mitigation.
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References · 11
- 2020Retrieval-Augmented Generation for Knowledge-Intensive NLP Taskscited by 9,160
- 2023Self-RAG: Learning to Retrieve, Generate, and Critique through Self-Reflectioncited by 1,800
- 2024MiniCheck: Efficient Fact-Checking of LLMs on Grounding Documentscited by 251
- 2023Factuality Challenges in the Era of Large Language Modelscited by 125
- 2024Can LLMs Produce Faithful Explanations For Fact-checking? Towards Faithful Explainable Fact-Checking via Multi-Agent Debatecited by 48
- 2024FACT-GPT: Fact-Checking Augmentation via Claim Matching with LLMscited by 27
- 2024Evidence-backed Fact Checking using RAG and Few-Shot In-Context Learning with LLMscited by 23
- 2024LLM-based Multi-Agent Systems: Techniques and Business Perspectivescited by 22
- 2024FoRAG: Factuality-optimized Retrieval Augmented Generation for Web-enhanced Long-form Question Answeringcited by 18
- 2024Lost in the Middle, and In-Between: Enhancing Language Models' Ability to Reason Over Long Contexts in Multi-Hop QAcited by 13
- 2025Towards Automated Fact-Checking of Real-World Claims: Exploring Task Formulation and Assessment with LLMscited by 5
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- 2025Fact-checking AI-generated news reports: Can LLMs catch their own lies?
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- 2025Hallucination to Truth: A Review of Fact-Checking and Factuality Evaluation in Large Language Models