  Large language models (LLMs) are proven to benefit a lot from
retrieval-augmented generation (RAG) in alleviating hallucinations confronted
with knowledge-intensive questions. RAG adopts information retrieval techniques
to inject external knowledge from semantic-relevant documents as input
contexts. However, due to today's Internet being flooded with numerous noisy
and fabricating content, it is inevitable that RAG systems are vulnerable to
these noises and prone to respond incorrectly. To this end, we propose to
optimize the retrieval-augmented Generator with a Adversarial Tuning
Multi-agent system (ATM). The ATM steers the Generator to have a robust
perspective of useful documents for question answering with the help of an
auxiliary Attacker agent. The Generator and the Attacker are tuned
adversarially for several iterations. After rounds of multi-agent iterative
tuning, the Generator can eventually better discriminate useful documents
amongst fabrications. The experimental results verify the effectiveness of ATM
and we also observe that the Generator can achieve better performance compared
to state-of-the-art baselines.
