  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challenges, including hallucination, outdated knowledge, and untraceable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.
