ATM : Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator (2024)

Abstract: 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.

## introduction large language models (llms) such as llama @xcite , @xcite , or gpt-4 @xcite have demonstrated impressive power in massive artificial intelligence tasks, especially the question answering. however, due to knowledge deficiency and dynamic updating, given knowledgeintensive or long-tail questions, llms often fail to provide concise answers, leading to rejection to answer @xcite or hallucination @xcite . ## atm system in this section, we introduce the adversarial tuning multi-agent system (atm), which contains two different agents optimized towards opposite directions: attacker fabricates fake knowledge into retrieved document; generator resists the perturbation and answers questions correctly. the two will serve as the protagonists of the subsequent adversarial tuning. ## multi-agent iterative tuning in this section, we present the adversarial tuning methodology of two agents. the attacker continuously increases the attack intensity, and gen-erator gradually improves its generation capability while resisting attack, resulting in an at-tacker with strong attack pattern and a genera-tor with great robustness against fabrications. ## conclusion in this paper, we propose a novel adversarial tuning multi-agent system (atm) to improve the robustness and capabilities of the retrieval-augmented generator. we conduct iterative optimization to improve the generator's accuracy and robustness. we also investigate the robustness of gener-ator under different settings in the detailed analysis, where the generator proves to be robust and powerful. analysis of the attacker also reveals that agents can be simultaneously optimized in an adversarial perspective. for future work, we plan to jointly optimize the retriever and generator to realize systematic robustness improvement.