Plug-and-Play Policy Planner for Large Language Model Powered Dialogue Agents

Understand

Proactive dialogues serve as a practical yet challenging dialogue problem in the era of large language models (LLMs), where the dialogue policy planning is the key to improving the proactivity of LLMs.

• Most existing studies enable the dialogue policy planning of LLMs using various prompting schemes or iteratively enhance this capability in handling the given case with verbal AI feedback.
• However, these approaches are either bounded by the policy planning capability of the frozen LLMs or hard to be transferred to new cases.
• In this work, we introduce a new dialogue policy planning paradigm to strategize LLMs for proactive dialogue problems with a tunable language model plug-in as a plug-and-play dialogue policy planner, named PPDPP.