Prompt-Based Monte-Carlo Tree Search for Goal-Oriented Dialogue Policy Planning
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Abstract (arXiv)
Planning for goal-oriented dialogue often requires simulating future dialogue interactions and estimating task progress. Many approaches thus consider training neural networks to perform look-ahead search algorithms such as A* search and Monte Carlo Tree Search (MCTS). However, this training often requires abundant annotated data, which creates challenges when faced with noisy annotations or low-resource settings. We introduce GDP-Zero, an approach using Open-Loop MCTS to perform goal-oriented dialogue policy planning without any model training. GDP-Zero prompts a large language model to act as a policy prior, value function, user simulator, and system model during the tree search. We evaluate GDP-Zero on the goal-oriented task PersuasionForGood, and find that its responses are preferred over ChatGPT up to 59.32% of the time, and are rated more persuasive than ChatGPT during interactive evaluations.
Key facts. Cited by 60 papers in the map (data through 7 Oct 2026). Builds on 35 of its 58 references that are in the map. First posted on arXiv 23 May 2023.
Builds on
35 of 58 references.
References · 35
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- 2018Deep Dyna-Q: Integrating Planning for Task-Completion Dialogue Policy Learningcited by 21
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- 2023Conversational Tree Search: A New Hybrid Dialog Taskcited by 7
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- 2023Commonsense-Aware Prompting for Controllable Empathetic Dialogue Generationcited by 2
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