Advancing Biomedical Claim Verification with LLMs

This technical report presents a structured four-step prompting strategy aimed at improving biomedical claim verification using large language models (LLMs). The document outlines the process of determining the entailment relationship between claims and evidence derived from medical studies or clinical trial reports. The proposed framework includes stages for claim comprehension, evidence analysis, intermediate conclusion, and entailment decision-making. Each step is designed to enhance logical consistency and factual grounding, thereby reducing reliance on memorization and improving the generalization of reasoning patterns across various tasks. The report details the evaluation of this framework on biomedical natural language inference (NLI) benchmarks, analyzing the distinct contributions of each reasoning step. The findings indicate that systematic prompting and well-structured instructions significantly enhance the interpretability and reliability of AI-driven biomedical claim verification, addressing challenges posed by complex medical documents and domain-specific terminology.