{"type":"ScholarlyArticle","title":"Generating Grounded Responses to Counter Misinformation via Learning Efficient Fine-Grained Critiques","authors":[{"name":"Xiaofei Xu","slug":"xiaofei-xu","given":"Xiaofei","family":"Xu","affiliation":""},{"name":"Xiuzhen Zhang","slug":"xiuzhen-zhang","given":"Xiuzhen","family":"Zhang","affiliation":""},{"name":"Ke Deng","slug":"ke-deng","given":"Ke","family":"Deng","affiliation":""}],"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, Misinformation 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. \r\nWe 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 additional results are available at https://github.com/xxfwin/MisMitiFact.","doi":"10.24963/ijcai.2025/1047","url":"https://ijcai.org/proceedings/2025/1047","pdf":"https://ijcai.org/proceedings/2025/1047.pdf","markdown":"https://ijcai.org/proceedings/2025/1047.md","bibtex":"https://ijcai.org/proceedings/2025/bibtex/1047","volume":{"slug":"2025","title":"Proceedings of the Thirty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2025","url":"https://ijcai.org/proceedings/2025/","year":2025,"location":"Montreal, Canada","dates":"16-22 August 2025","editors":"James Kwok","isbn":"978-1-956792-06-5","publisher":"International Joint Conferences on Artificial Intelligence Organization","doi_prefix":"10.24963/ijcai.2025"},"section":"AI4Tech: AI Enabling Technologies","track":"AI4Tech: AI Enabling Technologies","number":1047,"pages":{"first":9420,"last":9428},"keywords":["Domain-specific AI4Tech: AI4Social and AI4Society","Domain-specific AI4Tech: AI4Safety"],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}