Retrieval Augmented Language Models (RALMs) have gained significant attention for their ability to generate accurate answer and improve efficiency. However, RALMs are inherently vulnerable to imperfect information due to their reliance on the imperfect retriever or knowledge source. We identify three common scenarios-unanswerable, adversarial, conflicting-where retrieved document sets can confuse RALM with plausible real-world examples. We present the first comprehensive investigation to assess how well RALMs detect and handle such problematic scenarios. Among these scenarios, to systematically examine adversarial robustness we propose a new adversarial attack method, Generative model-based ADVersarial attack (GenADV) and a novel metric Robustness under Additional Document (RAD). Our findings reveal that RALMs often fail to identify the unanswerability or contradiction of a document set, which frequently leads to hallucinations. Moreover, we show the addition of an adversary significantly degrades RALM's performance, with the model becoming even more vulnerable when the two scenarios overlap (adversarial+unanswerable). Our research identifies critical areas for assessing and enhancing the robustness of RALMs, laying the foundation for the development of more robust models.

本研究解决了检索增强语言模型（RALMs）在不完美信息环境下的脆弱性，特别是在应对无法回答、对抗性和矛盾等问题场景中的表现不足。通过提出新的对抗攻击方法GenADV和稳健性评估指标RAD，研究揭示RALMs在识别文档集合的无法回答性或矛盾性方面的不足，指出需加强模型的鲁棒性，以提升其实用性。

朝着稳健的检索增强语言模型: 揭示不完美检索对检索增强语言模型的影响