In this paper, we introduce CIBER (Claim Investigation Based on Evidence Retrieval), an extension of the Retrieval-Augmented Generation (RAG) framework designed to identify corroborating and refuting documents as evidence for scientific claim verification. CIBER addresses the inherent uncertainty in Large Language Models (LLMs) by evaluating response consistency across diverse interrogation probes. By focusing on the behavioral analysis of LLMs without requiring access to their internal information, CIBER is applicable to both white-box and black-box models. Furthermore, CIBER operates in an unsupervised manner, enabling easy generalization across various scientific domains. Comprehensive evaluations conducted using LLMs with varying levels of linguistic proficiency reveal CIBER's superior performance compared to conventional RAG approaches. These findings not only highlight the effectiveness of CIBER but also provide valuable insights for future advancements in LLM-based scientific claim verification.

本研究提出了CIBER（基于证据检索的主张调查），旨在解决大型语言模型在科学主张验证中固有的不确定性。CIBER通过分析模型对不同查询的反应一致性，优化了证据检索过程，并在多种科学领域中实现了更好的性能，具有重要的实际应用潜力。

基于大型语言模型的科学主张验证的支持和反驳证据检索