This paper delves into an advanced implementation of Chain-of-Thought-Prompting in Large Language Models, focusing on the use of tools (or "plug-ins") within the explicit reasoning paths generated by this prompting method. We find that tool-enabled conversational agents often become sidetracked, as additional context from tools like search engines or calculators diverts from original user intents. To address this, we explore a concept wherein the user becomes the tool, providing necessary details and refining their requests. Through Conversation Analysis, we characterize this interaction as insert-expansion - an intermediary conversation designed to facilitate the preferred response. We explore possibilities arising from this 'user-as-a-tool' approach in two empirical studies using direct comparison, and find benefits in the recommendation domain.

翻译：本文深入探讨了大规模语言模型中思维链提示的一种高级实现，重点关注该提示方法在显式推理路径中如何利用工具（或称“插件”）。我们发现，工具增强的对话智能体常常会偏离主题，因为来自搜索引擎或计算器等工具的额外上下文会偏离用户的原始意图。为解决此问题，我们探索了一种将用户视为工具的概念，让用户提供必要的细节并优化自身请求。通过对话分析，我们将这种互动定性为“插入扩展”——一种旨在促成首选回复的中间对话。我们通过两项直接对比的实证研究，探索了这种“用户即工具”方法可能带来的可能性，并在推荐领域发现了其优势。