Retrieval-Augmented Generation (RAG) systems have shown promise in enhancing the performance of Large Language Models (LLMs). However, these systems face challenges in effectively integrating external knowledge with the LLM's internal knowledge, often leading to issues with misleading or unhelpful information. This work aims to provide a systematic study on knowledge checking in RAG systems. We conduct a comprehensive analysis of LLM representation behaviors and demonstrate the significance of using representations in knowledge checking. Motivated by the findings, we further develop representation-based classifiers for knowledge filtering. We show substantial improvements in RAG performance, even when dealing with noisy knowledge databases. Our study provides new insights into leveraging LLM representations for enhancing the reliability and effectiveness of RAG systems.

翻译：检索增强生成（RAG）系统在提升大型语言模型（LLM）性能方面展现出潜力。然而，这些系统在有效整合外部知识与LLM内部知识方面面临挑战，常常导致误导性或无用信息的问题。本研究旨在对RAG系统中的知识核查进行系统性探讨。我们对LLM的表征行为进行了全面分析，并论证了利用表征进行知识核查的重要性。基于这些发现，我们进一步开发了基于表征的知识过滤分类器。实验表明，即使在处理噪声知识数据库时，RAG性能也能获得显著提升。本研究为利用LLM表征增强RAG系统的可靠性与有效性提供了新的见解。