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.

翻译：训练大语言模型遵循用户指令已被证明能够赋予其流畅对话并与人类对齐的充分能力。然而，在指令双向流动的混合主导对话场景中（即大语言模型与用户相互发出指令），大语言模型如何引导基于计划的对话仍不完全清晰。本文探讨了一种双重目标混合主导对话场景：大语言模型不仅需将对话锚定于任意计划，还需同时满足程序性计划与用户指令。大语言模型需在引导用户完成计划的同时，适应新情境、回答问题，并在必要时激活安全护栏。我们提出了一种新型大语言模型，该模型能将对话扎根于程序性计划，主动掌握对话主导权，对系统行为施加安全护栏，同时改进对用户意外行为的响应能力。在受控设置与真实用户实验中的结果表明，我们命名为PlanLLM的最优模型相比强基线实现了2.1倍的性能提升。此外，实验还显示其在未见领域具有良好的泛化能力。