Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning
Fact verification (FV) aims to assess the veracity of a claim based on relevant evidence. The traditional approach for automated FV includes a three-part pipeline relying on short evidence snippets and encoder-only inference models. More recent approaches leverage the multi-turn nature of LLMs to address FV as a step-by-step problem where questions inquiring additional context are generated and answered until there is enough information to make a decision. This iterative method makes the verification process rational and explainable. While these methods have been tested for encyclopedic claims, exploration on domain-specific and realistic claims is missing. In this work, we apply an iterative FV system on three medical fact-checking datasets and evaluate it with multiple settings, including different LLMs, external web search, and structured reasoning using logic predicates. We demonstrate improvements in the final performance over traditional approaches and the high potential of step-by-step FV systems for domain-specific claims.
| Attribute | Value |
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| Address | Albuquerque, New Mexico, USA |
| Authors | Juraj Vladika , Ivana Hacajová , Prof. Dr. Florian Matthes |
| Citation | Juraj Vladika, Ivana Hacajova, and Florian Matthes. 2025. Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning. In Proceedings of the 2025 Conference of the Nations of the Americas Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 2: Short Papers), pages 805–816, Albuquerque, New Mexico. Association for Computational Linguistics. |
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| Research project | Scientific Claim Verification with Evidence from Text and Structured Knowledge (VeriSci) |
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| Type of publication | Conference |
| Year | 2025 |
| Publication URL | https://aclanthology.org/2025.naacl-short.68/ |
| Acronym | NAACL 2025 |
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