@inproceedings{ouyang-li-2023-autoplan,
title = "{A}uto{P}lan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models",
author = "Ouyang, Siqi and
Li, Lei",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.205",
doi = "10.18653/v1/2023.findings-emnlp.205",
pages = "3114--3128",
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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%0 Conference Proceedings
%T AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models
%A Ouyang, Siqi
%A Li, Lei
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F ouyang-li-2023-autoplan
%X 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.
%R 10.18653/v1/2023.findings-emnlp.205
%U https://aclanthology.org/2023.findings-emnlp.205
%U https://doi.org/10.18653/v1/2023.findings-emnlp.205
%P 3114-3128
Markdown (Informal)
[AutoPlan: Automatic Planning of Interactive Decision-Making Tasks With Large Language Models](https://aclanthology.org/2023.findings-emnlp.205) (Ouyang & Li, Findings 2023)
ACL