Logic-of-Thought: Empowering Large Language Models with Logic Programs for Solving Puzzles in Natural Language
This addresses the problem of precise reasoning in puzzles for AI systems, offering a hybrid approach that is incremental in integrating existing methods.
The paper tackles the challenge of solving complex puzzles in natural language by proposing Logic-of-Thought, a framework that combines large language models with logic programming to translate puzzles into answer set programs for accurate inference, achieving near-perfect accuracy on grid and dynamic puzzles.
Solving puzzles in natural language poses a long-standing challenge in AI. While large language models (LLMs) have recently shown impressive capabilities in a variety of tasks, they continue to struggle with complex puzzles that demand precise reasoning and exhaustive search. In this paper, we propose Logic-of-Thought (Logot), a novel framework that bridges LLMs with logic programming to address this problem. Our method leverages LLMs to translate puzzle rules and states into answer set programs (ASPs), the solution of which are then accurately and efficiently inferred by an ASP interpreter. This hybrid approach combines the natural language understanding of LLMs with the precise reasoning capabilities of logic programs. We evaluate our method on various grid puzzles and dynamic puzzles involving actions, demonstrating near-perfect accuracy across all tasks. Our code and data are available at: https://github.com/naiqili/Logic-of-Thought.