Towards Knowledge Checking in Retrieval-augmented Generation: A Representation Perspective
It addresses reliability issues in RAG systems for users relying on AI-generated information, but appears incremental as it builds on existing representation analysis for filtering.
This paper tackles the problem of integrating external knowledge with internal knowledge in Retrieval-Augmented Generation (RAG) systems, which often leads to misleading information, by developing representation-based classifiers for knowledge filtering and showing substantial improvements in RAG performance, even with noisy databases.
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.