Thought of Search: Planning with Language Models Through The Lens of Efficiency
arxiv(2024)
Abstract
Among the most important properties of algorithms investigated in computer
science are soundness, completeness, and complexity. These properties, however,
are rarely analyzed for the vast collection of recently proposed methods for
planning with large language models. In this work, we alleviate this gap. We
analyse these properties of using LLMs for planning and highlight that recent
trends abandon both soundness and completeness for the sake of inefficiency. We
propose a significantly more efficient approach that can, at the same time,
maintain both soundness and completeness. We exemplify on four representative
search problems, comparing to the LLM-based solutions from the literature that
attempt to solve these problems. We show that by using LLMs to produce the code
for the search components we can solve the entire datasets with 100% accuracy
with only a few calls to the LLM. We argue for a responsible use of compute
resources; urging research community to investigate sound and complete
LLM-based approaches that uphold efficiency.
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