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

Junda Zhu, Lingyong Yan, Haibo Shi, Dawei Yin, Lei Sha


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.
Anthology ID:
2024.emnlp-main.610
Volume:
Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2024
Address:
Miami, Florida, USA
Editors:
Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
10902–10919
Language:
URL:
https://aclanthology.org/2024.emnlp-main.610/
DOI:
10.18653/v1/2024.emnlp-main.610
Bibkey:
Cite (ACL):
Junda Zhu, Lingyong Yan, Haibo Shi, Dawei Yin, and Lei Sha. 2024. ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator. In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 10902–10919, Miami, Florida, USA. Association for Computational Linguistics.
Cite (Informal):
ATM: Adversarial Tuning Multi-agent System Makes a Robust Retrieval-Augmented Generator (Zhu et al., EMNLP 2024)
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PDF:
https://aclanthology.org/2024.emnlp-main.610.pdf
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 2024.emnlp-main.610.data.zip