Tool-augmented large language models (LLMs) have powered many applications. However, they are likely to suffer from knowledge conflict. In this paper, we propose a new type of knowledge conflict -- Tool-Memory Conflict (TMC), where the internal parametric knowledge contradicts with the external tool knowledge for tool-augmented LLMs. We find that existing LLMs, though powerful, suffer from TMC, especially on STEM-related tasks. We also uncover that under different conditions, tool knowledge and parametric knowledge may be prioritized differently. We then evaluate existing conflict resolving techniques, including prompting-based and RAG-based methods. Results show that none of these approaches can effectively resolve tool-memory conflicts.

翻译：工具增强型大语言模型已赋能众多应用，但它们很可能面临知识冲突问题。本文提出一种新型知识冲突——工具-记忆冲突，即工具增强型大语言模型的内部参数化知识与外部工具知识相矛盾。研究发现，现有大语言模型尽管功能强大，却普遍存在工具-记忆冲突问题，在STEM相关任务中尤为显著。研究还揭示，在不同条件下，工具知识与参数化知识可能被赋予不同的优先级。随后，我们对现有冲突解决技术（包括基于提示工程和基于检索增强生成的方法）进行了评估。结果表明，这些方法均无法有效解决工具-记忆冲突。