面向双目标对话场景的基于计划的 Large Language Models
Plan-Grounded Large Language Models for Dual Goal Conversational Settings
AI总结：
针对双目标混合主动对话，提出PlanLLM模型，将对话基于程序性计划并兼顾用户指令，实验显示比强基线提升2.1倍且泛化良好。
摘要：
训练大型语言模型（LLMs）遵循用户指令已被证明能够赋予LLM流畅对话并与人类对齐的充足能力。然而，在混合主动设置中，指令在对话的两个方向流动，即LLM和用户相互提供指令，LLM如何引导基于计划的对话尚不完全清楚。在本文中，我们应对一个双目标混合主动对话场景，其中LLM不仅将对话基于任意计划，还寻求同时满足程序性计划和用户指令。LLM负责引导用户完成计划，同时适应新情况、回答问题，并在需要时激活安全护栏。我们提出了一种新颖的LLM，它将对话基于程序性计划，能够主动发起对话，并对系统行为实施护栏，同时改进LLM对意外用户行为的响应。在受控设置和真实用户中的实验表明，我们称之为PlanLLM的最佳模型比强基线实现了2.1倍的改进。此外，实验还显示出对未见领域的良好泛化能力。
英文摘要：
Training Large Language Models (LLMs) to follow user instructions has been shown to supply the LLM with ample capacity to converse fluently while being aligned with humans. Yet, it is not completely clear how an LLM can lead a plan-grounded conversation in mixed-initiative settings where instructions flow in both directions of the conversation, i.e. both the LLM and the user provide instructions to one another. In this paper, we tackle a dual goal mixed-initiative conversational setting where the LLM not only grounds the conversation on an arbitrary plan but also seeks to satisfy both a procedural plan and user instructions. The LLM is then responsible for guiding the user through the plan and, at the same time, adapting to new circumstances, answering questions, and activating safety guardrails when needed. We propose a novel LLM that grounds the dialogue on a procedural plan, can take the dialogue initiative, and enforces guardrails on the system's behavior, while also improving the LLM's responses to unexpected user behavior. Experiments in controlled settings and with real users show that the best-performing model, which we call PlanLLM, achieves a 2.1x improvement over a strong baseline. Moreover, experiments also show good generalization to unseen domains.
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。
