  Retrieval-Augmented Generation (RAG) can alleviate hallucinations of Large
Language Models (LLMs) by referencing external documents. However, the
misinformation in external documents may mislead LLMs' generation. To address
this issue, we explore the task of "credibility-aware RAG", in which LLMs
automatically adjust the influence of retrieved documents based on their
credibility scores to counteract misinformation. To this end, we introduce a
plug-and-play method named $\textbf{Cr}$edibility-aware $\textbf{A}$ttention
$\textbf{M}$odification (CrAM). CrAM identifies influential attention heads in
LLMs and adjusts their attention weights based on the credibility of the
documents, thereby reducing the impact of low-credibility documents.
Experiments on Natual Questions and TriviaQA using Llama2-13B, Llama3-8B, and
Qwen-7B show that CrAM improves the RAG performance of LLMs against
misinformation pollution by over 20%, even surpassing supervised fine-tuning
methods.
