Plan of Thoughts: Heuristic-Guided Problem Solving with Large Language Models

• URL: http://arxiv.org/abs/2404.19055v1
• Date: Mon, 29 Apr 2024 18:51:17 GMT
• Title: Plan of Thoughts: Heuristic-Guided Problem Solving with Large Language Models
• Authors: Houjun Liu,
• Abstract summary: We formalize a planning-based approach to perform multi-step problem solving with language models. We demonstrate a superior success rate of 89.4% on the Game of 24 task as compared to existing approaches.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-sa/4.0/
• Abstract: While language models (LMs) offer significant capability in zero-shot reasoning tasks across a wide range of domains, they do not perform satisfactorily in problems which requires multi-step reasoning. Previous approaches to mitigate this involves breaking a larger, multi-step task into sub-tasks and asking the language model to generate proposals ("thoughts") for each sub-task and using exhaustive planning approaches such as DFS to compose a solution. In this work, we leverage this idea to introduce two new contributions: first, we formalize a planning-based approach to perform multi-step problem solving with LMs via Partially Observable Markov Decision Processes (POMDPs), with the LM's own reflections about the value of a state used as a search heuristic; second, leveraging the online POMDP solver POMCP, we demonstrate a superior success rate of 89.4% on the Game of 24 task as compared to existing approaches while also offering better anytime performance characteristics than fixed tree-search which is used previously. Taken together, these contributions allow modern LMs to decompose and solve larger-scale reasoning tasks more effectively.

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