Autoformalization of Game Descriptions using Large Language Models

Agnieszka Mensfelt; Kostas Stathis; Vince Trencsenyi

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arXiv · 2409.12300

Autoformalization of Game Descriptions using Large Language Models

Agnieszka Mensfelt·Kostas Stathis·Vince Trencsenyi

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Abstract

Game theory is a powerful framework for reasoning about strategic interactions, with applications in domains ranging from day-to-day life to international politics. However, applying formal reasoning tools in such contexts is challenging, as these scenarios are often expressed in natural language. To address this, we introduce a framework for the autoformalization of game-theoretic scenarios, which translates natural language descriptions into formal logic representations suitable for formal solvers. Our approach utilizes one-shot prompting and a solver that provides feedback on syntactic correctness to allow LLMs to refine the code. We evaluate the framework using GPT-4o and a dataset of natural language problem descriptions, achieving 98% syntactic correctness and 88% semantic correctness. These results show the potential of LLMs to bridge the gap between real-life strategic interactions and formal reasoning.

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Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi. 2024-09-18. Autoformalization of Game Descriptions using Large Language Models. https://arxiv.org/abs/2409.12300

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