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Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques
Conference paper   Open access

Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques

Xiaofei Xu, Xiuzhen Zhang and Ke Deng
Thirty-Fourth International Joint Conference on Artificial Intelligence AI4Tech: AI Enabling Technologies (Montreal, Canada, 16/08/2025–22/08/2025)
08/2025
DOI: https://doi.org/10.24963/ijcai.2025/1047
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Abstract

Fake news and misinformation poses a significant threat to society, making efficient mitigation essential. However, manual fact-checking is costly and lacks scalability. Large Language Models (LLMs) offer promise in automating counter-response generation to mitigate misinformation, but a critical challenge lies in their tendency to hallucinate non-factual information. Existing models mainly rely on LLM self-feedback to reduce hallucination, but this approach is computationally expensive. In this paper, we propose MisMitiFact, Misinforma-tion Mitigation grounded in Facts, an efficient framework for generating fact-grounded counter-responses at scale. MisMitiFact generates simple critique feedback to refine LLM outputs, ensuring responses are grounded in evidence. We develop lightweight, fine-grained critique models trained on data sourced from readily available fact-checking sites to identify and correct errors in key elements such as numerals, entities, and topics in LLM generations. Experiments show that MisMitiFact generates counter-responses of comparable quality to LLMs' self-feedback while using significantly smaller critique models. Importantly, it achieves ∼5x increase in feedback generation throughput, making it highly suitable for cost-effective, large-scale misinformation mitigation. Code and LLM prompt templates are at https://github.com/xxfwin/ MisMitiFact.

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