By leveraging the retrieval of information from external knowledge databases, Large Language Models (LLMs) exhibit enhanced capabilities for accomplishing many knowledge-intensive tasks. However, due to the inherent flaws of current retrieval systems, there might exist irrelevant information within those retrieving top-ranked passages. In this work, we present a comprehensive investigation into the robustness of LLMs to different types of irrelevant information under various conditions. We initially introduce a framework to construct high-quality irrelevant information that ranges from semantically unrelated, partially related, and related to questions. Furthermore, our analysis demonstrates that the constructed irrelevant information not only scores highly on similarity metrics, being highly retrieved by existing systems, but also bears semantic connections to the context. Our investigation reveals that current LLMs still face challenges in discriminating highly semantically related information and can be easily distracted by these irrelevant yet misleading contents. Besides, we also find that current solutions for handling irrelevant information have limitations in improving the robustness of LLMs to such distractions. Resources are available at https://github.com/Di-viner/LLM-Robustness-to-Irrelevant-Information.

翻译：通过从外部知识数据库中检索信息，大语言模型在完成许多知识密集型任务时展现出增强的能力。然而，由于当前检索系统固有的缺陷，在排序靠前的检索结果中可能存在无关信息。本研究全面探究了大语言模型在不同条件下对各类无关信息的鲁棒性。我们首先提出一个框架，用于构建从语义无关、部分相关到与问题相关的各类高质量无关信息。进一步分析表明，所构建的无关信息不仅在相似度指标上得分很高，易被现有系统检索到，而且与上下文存在语义关联。我们的研究发现，当前的大语言模型在区分高语义相关信息时仍面临挑战，并且容易被这些无关但具有误导性的内容干扰。此外，我们也发现当前处理无关信息的方案在提升大语言模型对此类干扰的鲁棒性方面存在局限性。相关资源可访问 https://github.com/Di-viner/LLM-Robustness-to-Irrelevant-Information。