个人计划的对话式规划
Conversational Planning for Personal Plans
AI总结：
本文提出一种以LLM为元控制器、工具增强选项策略执行宏动作的对话式规划架构，用于长期个人计划的适应性规划，并应用于辅导和健康教练场景。
摘要：
大型语言模型（LLMs）的语言生成和推理能力使得对话系统在多种任务中表现出色，从代码生成、撰写文章、通过STEM和法律考试，到知识搜索的新范式。除了这些短期应用外，LLMs越来越多地被用于帮助实现需要长时间完成的现实生活目标或任务，这些任务涉及跨天数、周数、月数甚至数年的多次会话。因此，为了实现对话系统的长期交互和任务，我们需要能够规划长时域的语言智能体。传统上，这种能力由具有分层规划能力的强化学习智能体提供。在这项工作中，我们探索了一种新颖的架构，其中LLM充当元控制器，决定智能体的下一个宏动作，而工具增强的基于LLM的选项策略执行所选的宏动作。我们针对一组特定的宏动作实例化此框架，通过对话和后续问题收集用户反馈，实现用户个人计划的适应性规划。我们展示了该范式如何适用于从学术和非学术任务的辅导到个人健康计划的对话式教练等场景。
英文摘要：
The language generation and reasoning capabilities of large language models (LLMs) have enabled conversational systems with impressive performance in a variety of tasks, from code generation, to composing essays, to passing STEM and legal exams, to a new paradigm for knowledge search. Besides those short-term use applications, LLMs are increasingly used to help with real-life goals or tasks that take a long time to complete, involving multiple sessions across days, weeks, months, or even years. Thus to enable conversational systems for long term interactions and tasks, we need language-based agents that can plan for long horizons. Traditionally, such capabilities were addressed by reinforcement learning agents with hierarchical planning capabilities. In this work, we explore a novel architecture where the LLM acts as the meta-controller deciding the agent's next macro-action, and tool use augmented LLM-based option policies execute the selected macro-action. We instantiate this framework for a specific set of macro-actions enabling adaptive planning for users' personal plans through conversation and follow-up questions collecting user feedback. We show how this paradigm can be applicable in scenarios ranging from tutoring for academic and non-academic tasks to conversational coaching for personal health plans.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
