Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning (2025)

Abstract: 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.

## introduction the digital age has been marked by the rise and spread of online misinformation, which has negative societal consequences, especially when related to public health @xcite . fact verification (fv) has emerged as an automated approach for addressing the increasing rate of deceptive content promulgated online @xcite . on top of that, fv can help improve the factuality of generative large language models @xcite and help scientists find reliable evidence for assessing their research hypotheses @xcite . the common pipeline for automated fact verification consists of document retrieval, evidence extraction, veracity prediction, and optionally justification production @xcite . in such a setup, document retrieval is usually done with a claim: a mutation in hnf4a leads to an increased risk of developing diabetes by the age of 14 years. predicate generation: onsetage(diabetes, 14), increaserisk(hnf4a mutation, diabetes) ## foundations in this section, we describe in more detail the two fv approaches: the conventional three-part pipeline, serving as a baseline, and the step-by-step llm-based system, which we mainly use. ## experiments and setup in the experiments, our main research question is rq: does the iterative llm approach outperform the traditional three-part pipeline for domain-specific fact verification? on top of that, we test three further aspects of the system: (a) knowledge source, (b) structured reasoning, and (c) base llm. the knowledge sources include: internal knowledge of the llm and the online search of the whole web. our search engine of choice is duckduckgo, an open-source tool focused on privacy. we use it through a dedicated python library. 2 this search engine provided a smooth search experience with no interruptions, and we deemed the quality of the retrieved results similar to the more popular google or bing for our use case. we take the provided snippets from the first 5 results and give them as input evidence to the reasoner llm. the structured reasoning in (b) refers to using logic predicates, as described in the previous section. all the experiments in (a) and (b) were done using gpt-4o-mini-2024-07-18 as the base llm, the model from openai with good reasoning capabilities @xcite . in experiment round (c), we additionally test normal reasoning with internal knowledge and online search using mixtral 8x7b @xcite , a highly performing open-weights model based on a mixture-of-experts architecture, and llama 3.1 (70b) (meta, 2024), a recent advanced openweights model from meta. we use gpt through the openai api and the two other models through the together ai api, foot_2 setting temperature to 0 for best reproducibility and maximum tokens to 512. we use these llms for all parts of the fact verification process, i.e. for all steps m q , m s , m r as described in the previous section. all the used prompts are in the appendix. all experiments were run on one nvidia v100 gpu with 16 gb vram. ## results and discussion the first three rows of improvement. as seen in ## conclusion in this study, we develop a step-by-step system for fact verification based on iterative question generation and explainable reasoning. we apply the system on three medical fact-checking datasets and test different settings. we show that by utilizing llms, this system can create follow-up questions on complex concepts and relations from the claims in order to gather background evidence, reason over newly discovered evidence, and finally lead to predictions that achieve higher results when compared to traditional pipelines. we hope that our study encourages more exploration of advanced systems for domain-specific fact verification.