StateAct: State Tracking and Reasoning for Acting and Planning with Large Language Models

• URL: http://arxiv.org/abs/2410.02810v1
• Date: Sat, 21 Sep 2024 05:54:35 GMT
• Title: StateAct: State Tracking and Reasoning for Acting and Planning with Large Language Models
• Authors: Nikolai Rozanov, Marek Rei,
• Abstract summary: Planning and acting to solve real' tasks using large language models (LLMs) in interactive environments has become a new frontier for AI methods. We propose a simple method based on few-shot in-context learning alone to enhance chain-of-thought' with state-tracking for planning and acting with LLMs.
• Score: 10.359008237358603
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Planning and acting to solve `real' tasks using large language models (LLMs) in interactive environments has become a new frontier for AI methods. While recent advances allowed LLMs to interact with online tools, solve robotics tasks and many more, long range reasoning tasks remain a problem for LLMs. Existing methods to address this issue are very resource intensive and require additional data or human crafted rules, instead, we propose a simple method based on few-shot in-context learning alone to enhance `chain-of-thought' with state-tracking for planning and acting with LLMs. We show that our method establishes the new state-of-the-art on Alfworld for in-context learning methods (\textbf{+14\%} over the previous best few-shot in-context learning method) and performs on par with methods that use additional training data and additional tools such as code-execution. We also demonstrate that our enhanced `chain-of-states' allows the agent to both solve longer horizon problems and to be more efficient in number of steps required to solve a task. We show that our method works across a variety of LLMs for both API-based and open source ones. Finally, we also conduct ablation studies and show that `chain-of-thoughts' helps state-tracking accuracy, while a json-structure harms overall performance. We open-source our code and annotations at \url{https://github.com/ai-nikolai/StateAct}.

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