Large language models (LLMs) have demonstrated exceptional performance in planning the use of various functional tools, such as calculators and retrievers, particularly in question-answering tasks. In this paper, we expand the definition of these tools, centering on conceptual tools within the context of dialogue systems. A conceptual tool specifies a cognitive concept that aids systematic or investigative thought. These conceptual tools play important roles in practice, such as multiple psychological or tutoring strategies being dynamically applied in a single turn to compose helpful responses. To further enhance the reasoning and planning capability of LLMs with these conceptual tools, we introduce a multi-persona collaboration framework: Think-Plan-Execute (TPE). This framework decouples the response generation process into three distinct roles: Thinker, Planner, and Executor. Specifically, the Thinker analyzes the internal status exhibited in the dialogue context, such as user emotions and preferences, to formulate a global guideline. The Planner then generates executable plans to call different conceptual tools (e.g., sources or strategies), while the Executor compiles all intermediate results into a coherent response. This structured approach not only enhances the explainability and controllability of responses but also reduces token redundancy. We demonstrate the effectiveness of TPE across various dialogue response generation tasks, including multi-source (FoCus) and multi-strategy interactions (CIMA and PsyQA). This reveals its potential to handle real-world dialogue interactions that require more complicated tool learning beyond just functional tools. The full code and data will be released for reproduction.

翻译：大型语言模型在规划使用计算器、检索器等多种功能性工具方面展现出卓越性能，尤其在问答任务中表现突出。本文拓展了工具的定义范畴，聚焦对话系统中的概念性工具。概念性工具指代辅助系统性或探究性思维的认知概念，这些工具在实践中具有重要作用——例如在同一轮对话中动态应用多种心理学或教学策略以构建有益回应。为进一步提升语言模型使用概念工具进行推理和规划的能力，我们提出多角色协作框架：思考-规划-执行（Think-Plan-Execute, TPE）。该框架将响应生成过程解耦为三个独立角色：思考者、规划者和执行者。具体而言，思考者通过分析对话语境中的内在状态（如用户情绪与偏好）制定全局准则；规划者据此生成可执行方案以调用不同概念工具（如信息源或策略）；执行者则将中间结果整合为连贯响应。这种结构化方法不仅增强了响应的可解释性与可控性，还降低了令牌冗余。我们在多源交互（FoCus）与多策略交互（CIMA和PsyQA）等对话响应生成任务中验证了TPE的有效性，揭示了该方法在需要超越功能性工具的复合工具学习的真实对话交互中的应用潜力。完整代码与数据将公开发布以供复现。