Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning (2502.14765v1)

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.

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

The paper "Step-by-Step Fact Verification System for Medical Claims with Explainable Reasoning" presents a novel approach to automated fact verification (FV) with a particular emphasis on domain-specific medical claims. Traditional FV systems are limited by their reliance on short evidence snippets and encoder-only inference models, often unsuitable for the intricacies of domain-specific claims. This research addresses these limitations through a step-by-step methodology, utilizing LLMs to iteratively gather context and evidence, thereby producing more accurate and explainable verification results.

Methodological Advancements

The methodology employs the multi-turn capabilities of LLMs, redefining FV as an iterative process. In this approach, new questions are generated at each step to source additional evidence until sufficient information is gathered for a conclusive decision. This iterative questioning not only enhances the system's ability to deal with complex claims but also enables the generation of detailed explanations, making the reasoning process transparent and accessible.

Three medical fact-checking datasets serve as the basis for testing the effectiveness of this approach: SciFact, HealthFC, and CoVERT. The datasets encompass a range of medical claims from scientific literature, public health, and social media, offering a comprehensive evaluation environment.

Empirical Results

The results demonstrate that the proposed system surpasses traditional FV pipelines, particularly in scenarios requiring detailed evidence retrieval and complex reasoning. Notably, the paper reports performance improvements of 4.3% to 4.9% in F1 scores across the datasets when leveraging the iterative LLM-based approach compared to conventional methods.

Enhancements are further observed when incorporating structured reasoning, specifically through the use of logic predicates that aid in querying and synthesizing information. However, the effectiveness of this approach is contingent on the complexity and clarity of the claims, with simpler or more straightforwardly written claims benefiting the most.

Implications and Speculations

The findings suggest significant implications for the application of FV systems in the medical domain. By enhancing the accuracy and transparency of FV processes, this approach holds potential for improving the reliability of information disseminated in healthcare and other critical areas. The paper advocates for the integration of more advanced, contextually aware LLMs capable of intricate reasoning processes to support FV tasks across various domains.

Future developments could explore the integration of structured knowledge bases, such as medical ontologies or knowledge graphs, to further bolster evidence retrieval and reasoning capabilities. Additionally, there is scope for expanding the system’s applicability by addressing not enough information (NEI) scenarios, a common challenge in real-world FV contexts.

Conclusion

This research contributes significantly to the domain of automated fact verification, particularly for medical claims. By employing a step-by-step reasoning framework, the paper provides a robust mechanism that not only improves FV accuracy but also adheres to the growing demand for explainable AI systems in critical domains. The implications for enhancing digital literacy and the quality of online information are both timely and profound, warranting further exploration and development in this direction.