Autoformalization of Game Descriptions using Large Language Models

• URL: http://arxiv.org/abs/2409.12300v1
• Date: Wed, 18 Sep 2024 20:18:53 GMT
• Title: Autoformalization of Game Descriptions using Large Language Models
• Authors: Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi,
• Abstract summary: We introduce a framework for the autoformalization of game-theoretic scenarios. This translates natural language descriptions into formal logic representations suitable for formal solvers. We evaluate the framework using GPT-4o and a dataset of natural language problem descriptions.
• Score: 3.5083201638203154
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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