Summary of Autoformalization Of Game Descriptions Using Large Language Models, by Agnieszka Mensfelt and Kostas Stathis and Vince Trencsenyi
Autoformalization of Game Descriptions using Large Language Models
by Agnieszka Mensfelt, Kostas Stathis, Vince Trencsenyi
First submitted to arxiv on: 18 Sep 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Computer Science and Game Theory (cs.GT)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed framework for autoformalization of game-theoretic scenarios translates natural language descriptions into formal logic representations, enabling the application of formal reasoning tools in domains like international politics. The approach uses one-shot prompting and a solver providing feedback on syntactic correctness to refine code generated by LLMs. The framework is evaluated using GPT-4o and a dataset of problem descriptions, achieving 98% syntactic correctness and 88% semantic correctness. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The researchers created a new way to turn language descriptions into formal logic representations that can be used in game theory. This helps bridge the gap between real-life strategic interactions and formal reasoning tools. They tested this approach using a large language model (LLM) called GPT-4o, which was able to generate code with 98% accuracy. Overall, this new framework has potential applications in fields like international politics. |
Keywords
» Artificial intelligence » Gpt » Large language model » One shot » Prompting
