Large language model (LLM) has proven to benefit a lot from retrieval augmentation in alleviating hallucinations confronted with knowledge-intensive questions. Retrieval-augmented generation (RAG) adopts IR-based techniques utilizing semantic-relevant documents as the generator's input context and realizes external knowledge injection. However, on today's Internet which is flooded with content generated by LLMs, there are too many "related yet useless" documents or even fake knowledge fabricated by LLMs, which will introduce extra noise to the generator and distract it from giving correct results. To this end, we regard the training of the RAG generator model as a multi-agent adversarial-defensive system, guiding the generator to have a better taste of whether a specific document helps answer the question through the Adversarial Tuning in a Multi-agent (ATM) system to strengthen the generator's robustness in an RAG pipeline. After rounds of multi-agent iterative tuning, we find that the ATM Generator can eventually discriminate useful documents amongst LLM fabrications and achieve better performance than strong baselines.

翻译：大型语言模型（LLM）已证明通过检索增强技术能够有效缓解其在面对知识密集型问题时的幻觉现象。检索增强生成（RAG）采用基于信息检索的技术，将语义相关的文档作为生成器的输入上下文，从而实现外部知识注入。然而，在当今充斥着LLM生成内容的互联网环境中，存在大量“相关但无用”的文档，甚至包含LLM捏造的虚假知识，这些信息会给生成器引入额外噪声，干扰其给出正确结果。为此，我们将RAG生成器模型的训练视为一个多智能体对抗-防御系统，通过多智能体对抗性调优（ATM）系统引导生成器更好地判断特定文档是否有助于回答问题，从而增强其在RAG流程中的鲁棒性。经过多轮多智能体迭代调优后，我们发现ATM生成器最终能够从LLM生成的虚构内容中识别出有用文档，并取得优于强基线的性能表现。