Purify the Source: Soft External Knowledge Detoxification Against Retrieval Pollution

In order to address the challenges of hallucination and information missing in Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) has become a standard paradigm. However, RAG systems face a critical threat, that is, the pollution within external corpora, which underscores the urgent need for robust RAG reinforcement. While existing strategies often resort to hard filtering to exclude polluted documents, which is difficult to properly eliminate polluted content and may discard valuable information, undermining overall system effectiveness. In this paper, we propose SeKe, a novel soft external knowledge detoxification approach designed to mitigate retrieval pollution. Inspired by classic convolution and pooling mechanisms, SeKe employs knowledge convolution alongside generative semantic pooling operations, which can progressively aggregate content within retrieved documents by leveraging the distributional differences between polluted and benign documents, thereby reducing the overall density and concentration of pollution and lowering the susceptibility of LLMs to polluted information. Experimental results show that SeKe can achieve promising performance in almost all 15 investigated pollution settings. Furthermore, SeKe can seamlessly integrate with other RAG frameworks, yielding an average absolute accuracy increase of 14% and reducing the misleading rate by 19%, proving its effectiveness and compatibility.