By providing external information to large language models (LLMs), tool augmentation (including retrieval augmentation) has emerged as a promising solution for addressing the limitations of LLMs' static parametric memory. However, how receptive are LLMs to such external evidence, especially when the evidence conflicts with their parametric memory? We present the first comprehensive and controlled investigation into the behavior of LLMs when encountering knowledge conflicts. We propose a systematic framework to elicit high-quality parametric memory from LLMs and construct the corresponding counter-memory, which enables us to conduct a series of controlled experiments. Our investigation reveals seemingly contradicting behaviors of LLMs. On the one hand, different from prior wisdom, we find that LLMs can be highly receptive to external evidence even when that conflicts with their parametric memory, given that the external evidence is coherent and convincing. On the other hand, LLMs also demonstrate a strong confirmation bias when the external evidence contains some information that is consistent with their parametric memory, despite being presented with conflicting evidence at the same time. These results pose important implications that are worth careful consideration for the further development and deployment of tool- and retrieval-augmented LLMs.

翻译：通过向大语言模型（LLMs）提供外部信息，工具增强（包括检索增强）已成为解决LLMs静态参数化内存局限性的有前景方案。然而，LLMs对外部证据的接受程度如何，尤其是当证据与参数化内存冲突时？我们首次对LLMs在遇到知识冲突时的行为进行了全面且受控的研究。我们提出一个系统性框架，从LLMs中提取高质量参数化内存并构建相应的反内存，从而开展一系列受控实验。我们的研究揭示了LLMs看似矛盾的行为：一方面，与以往认知不同，我们发现当外部证据连贯且有说服力时，即使与参数化内存冲突，LLMs也能高度接受；另一方面，当外部证据包含与参数化内存一致的信息时，即便同时呈现冲突证据，LLMs也表现出强烈的确认偏误。这些结果对于工具增强和检索增强LLMs的进一步开发与部署提出了值得深思的重要启示。