Recognizing poker as a paradigm within imperfect information games (IIGs), ‘PokerGPT: An End-to-End Lightweight Solver for Multi-Player Texas Hold’em via Large Language Model’ offers a solution that deploys a lightweight LLM for strategic advice generation. PokerGPT stands out by requiring only simple textual information, paving the way for convenient AI-human interaction.

• Introduces PokerGPT, an end-to-end solver for multi-player Poker games.
• Efficiently uses LLMs for decision-making in Texas Hold’em.
• Demonstrates superior performance in terms of win rates and response time.

This study is groundbreaking as it illustrates the vast potential of LLMs in tackling complex strategy-based games, setting a precedent for future research and applications in the field of IIGs.