Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training
<|reference_start|>Enhancing Noise Robustness of Retrieval-Augmented Language Models with Adaptive Adversarial Training: Large Language Models (LLMs) exhibit substantial capabilities yet encounter challenges, including hallucination, outdated knowledge, and untraceable reasoning processes. Retrieval-augmented generation (RAG) has emerged as a promising solution, integrating knowledge from external databases to mitigate these challenges. However, inappropriate retrieved passages can potentially hinder the LLMs' capacity to generate comprehensive and high-quality responses. Prior RAG studies on the robustness of retrieval noises often confine themselves to a limited set of noise types, deviating from real-world retrieval environments and limiting practical applicability. In this study, we initially investigate retrieval noises and categorize them into three distinct types, reflecting real-world environments. We analyze the impact of these various retrieval noises on the robustness of LLMs. Subsequently, we propose a novel RAG approach known as Retrieval-augmented Adaptive Adversarial Training (RAAT). RAAT leverages adaptive adversarial training to dynamically adjust the model's training process in response to retrieval noises. Concurrently, it employs multi-task learning to ensure the model's capacity to internally recognize noisy contexts. Extensive experiments demonstrate that the LLaMA-2 7B model trained using RAAT exhibits significant improvements in F1 and EM scores under diverse noise conditions. For reproducibility, we release our code and data at: https://github.com/calubkk/RAAT.<|reference_end|>
@article{fang2024enhancing,
    title={Enhancing Noise Robustness of Retrieval-Augmented Language Models with
  Adaptive Adversarial Training},
    author={Feiteng Fang, Yuelin Bai, Shiwen Ni, Min Yang, Xiaojun Chen and
  Ruifeng Xu},
    journal={ACL 2024, Main Conference},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.20978},
    primaryClass={cs.AI}
}