AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models

• URL: http://arxiv.org/abs/2305.15064v3
• Date: Thu, 26 Oct 2023 16:44:39 GMT
• Title: AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models
• Authors: Siqi Ouyang and Lei Li
• Abstract summary: AutoPlan is an approach to guide LLM-based agents to accomplish interactive decision-making tasks. Our experiments show that AutoPlan achieves success rates on par with the baselines.
• Score: 11.895111124804503
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent large language models (LLMs) are promising for making decisions in grounded environments. However, LLMs frequently fail in complex decision-making tasks due to the misalignment between the pre-trained knowledge in LLMs and the actual rules in the environment. Existing methods require either costly gradient computation or lengthy in-context demonstrations. In this paper, we propose AutoPlan, an approach to guide LLM-based agents to accomplish interactive decision-making tasks. AutoPlan augments the LLM prompt with a task-solving plan and optimizes it through iterative experience collection and reflection. Our experiments show that AutoPlan, though using no in-context demonstrations, achieves success rates on par with the baselines using human-written demonstrations on ALFWorld and even outperforms them by 8% on HotpotQA. The code is available at https://github.com/owaski/AutoPlan.

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