TPE: Towards Better Compositional Reasoning over Conceptual Tools with
  Multi-persona Collaboration
Hongru Wang Huimin Wang Lingzhi Wang Minda Hu Rui Wang Boyang Xue Hongyuan Lu Fei Mi Kam-Fai Wong
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.