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：通过向大语言模型提供外部信息，工具增强（包括检索增强）已成为解决大语言模型静态参数记忆局限性的有前景方案。然而，当外部证据与参数记忆相冲突时，大语言模型对这些证据的接收程度如何？我们首次对知识冲突情境下大语言模型的行为展开全面且受控的研究。我们提出系统性框架，从大语言模型中提取高质量参数记忆并构建对应的反记忆，从而实施一系列受控实验。研究揭示了大语言模型看似矛盾的行为：一方面，与既有认知不同，我们发现当外部证据连贯且具有说服力时，即使与参数记忆冲突，大语言模型仍能高度接收这些证据；另一方面，当外部证据包含与参数记忆一致的信息时，即便同时呈现冲突证据，大语言模型仍表现出强烈的确认偏差。这些结果对工具增强与检索增强大语言模型的进一步开发与部署具有重要启示意义，值得审慎考量。