Controllable Conversations: Planning-Based Dialogue Agent with Large Language Models

• URL: http://arxiv.org/abs/2407.03884v2
• Date: Sun, 22 Dec 2024 17:34:01 GMT
• Title: Controllable Conversations: Planning-Based Dialogue Agent with Large Language Models
• Authors: Zhigen Li, Jianxiang Peng, Yanmeng Wang, Yong Cao, Tianhao Shen, Minghui Zhang, Linxi Su, Shang Wu, Yihang Wu, Yuqian Wang, Ye Wang, Wei Hu, Jianfeng Li, Shaojun Wang, Jing Xiao, Deyi Xiong,
• Abstract summary: Planning-based Conversational Agents (PCA) is a dialogue framework aimed at enhancing controllability of LLM-driven agents. We propose a dataset comprising SOP-annotated multi-scenario dialogues, generated using a semi-automated role-playing system with GPT-4o. We also propose a novel method that integrates Chain of Thought reasoning with supervised fine-tuning for SOP prediction and utilizes Monte Carlo Tree Search for optimal action planning during dialogues.
• Score: 52.7201882529976
• License:
• Abstract: Conversational agents powered by Large Language Models (LLMs) show superior performance in various tasks. Despite the better user understanding and human-like responses, their lack of controllability remains a key challenge, often leading to unfocused conversations or task failure. To address this challenge, we propose Planning-based Conversational Agents (PCA), a novel dialogue framework aimed at enhancing the controllability of LLM-driven agents. Specifically, our approach introduces Standard Operating Procedure (SOP) to regulate dialogue flow. To enable PCA to learn SOP, we curate a dataset comprising SOP-annotated multi-scenario dialogues, generated using a semi-automated role-playing system with GPT-4o and validated through strict manual quality control. Additionally, we propose a novel method that integrates Chain of Thought reasoning with supervised fine-tuning for SOP prediction and utilizes Monte Carlo Tree Search for optimal action planning during dialogues. Experimental results demonstrate the effectiveness of our method, such as achieving a 27.95% improvement in action accuracy compared to baseline models based on GPT-3.5 and also showing notable gains for open-source models. Dataset and codes are publicly available.

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