Large Language Models as Common-Sense Heuristics

• URL: http://arxiv.org/abs/2501.18816v1
• Date: Fri, 31 Jan 2025 00:26:38 GMT
• Title: Large Language Models as Common-Sense Heuristics
• Authors: Andrey Borro, Patricia J Riddle, Michael W Barley, Michael J Witbrock,
• Abstract summary: Large Language Models (LLMs) possess parametrised knowledge across a wide range of topics, enabling them to leverage the natural language descriptions of planning tasks in their solutions. We introduce a novel planning method, which leverages the parametrised knowledge of LLMs by using their output as a for Hill-Climbing Search. Our method outperforms the task success rate of similar systems within a common household environment by 22 percentage points, with consistently executable plans.
• Score: 0.9093413254392775
• License:
• Abstract: While systems designed for solving planning tasks vastly outperform Large Language Models (LLMs) in this domain, they usually discard the rich semantic information embedded within task descriptions. In contrast, LLMs possess parametrised knowledge across a wide range of topics, enabling them to leverage the natural language descriptions of planning tasks in their solutions. However, current research in this direction faces challenges in generating correct and executable plans. Furthermore, these approaches depend on the LLM to output solutions in an intermediate language, which must be translated into the representation language of the planning task. We introduce a novel planning method, which leverages the parametrised knowledge of LLMs by using their output as a heuristic for Hill-Climbing Search. This approach is further enhanced by prompting the LLM to generate a solution estimate to guide the search. Our method outperforms the task success rate of similar systems within a common household environment by 22 percentage points, with consistently executable plans. All actions are encoded in their original representation, demonstrating that strong results can be achieved without an intermediate language, thus eliminating the need for a translation step.

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