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Artificial intelligence (AI) is revolutionizing many fields, and now it's conquering the world of poker. A new benchmarking tool called PokerBench allows researchers to evaluate and improve the poker-playing abilities of large language models (LLMs). Poker, a game of incomplete information, requires a combination of mathematical understanding, strategic thinking, planning, and insight into human psychology. This complexity makes poker an ideal testing ground for the further development of LLMs.

PokerBench: A New Benchmark for AI in Poker

PokerBench is a comprehensive benchmark consisting of 11,000 relevant poker scenarios developed in collaboration with experienced poker players. The scenarios are divided into pre-flop and post-flop moves and cover a wide range of game situations. By evaluating the performance of LLMs in these scenarios, researchers can identify the strengths and weaknesses of the models and make targeted improvements.

LLMs Put to the Test: From GPT-4 to Llama

The initial results of the PokerBench evaluation show that current LLMs, including prominent models like GPT-4, ChatGPT 3.5, and various Llama and Gemma models, are not yet capable of playing optimal poker. While they excel in traditional NLP tasks, they reach their limits with the strategic complexity of poker. Interestingly, the models show significant improvements in their gameplay after fine-tuning. This suggests that LLMs have the potential to learn high-level poker if trained with the right methods.

Validation Through Competition

The validity of PokerBench was confirmed by directly comparing models with different scores. In simulated poker games, models with higher PokerBench scores consistently showed higher win rates. This underscores the significance of the benchmark as an indicator of the poker skills of LLMs.

Limits of Supervised Fine-Tuning

The project also highlighted the limitations of simple supervised fine-tuning. Games between a fine-tuned model and GPT-4 demonstrated that this method alone is not sufficient to learn an optimal game strategy. More advanced training methods, such as incorporating reinforcement learning or learning through self-play, are necessary to fully exploit the potential of LLMs in poker.

Outlook: AI as a Professional Poker Player?

PokerBench offers a valuable resource for the research and development of AI in poker. The benchmark allows for quick and reliable assessment of the poker skills of LLMs and serves as a basis for the development of new training methods. The research results suggest that LLMs have the potential to challenge professional poker players in the future. Whether and when this goal will be achieved remains to be seen. However, the development of PokerBench and the associated research results are an important step in this direction.

For Mindverse, a German company specializing in AI-powered content creation, image generation, and research, PokerBench offers an exciting opportunity to push the boundaries of AI technology. Mindverse develops customized AI solutions, including chatbots, voicebots, AI search engines, and knowledge systems. The application of LLMs in the complex environment of poker opens new perspectives for the development of even more powerful AI systems.

Bibliography: - https://x.com/akshatgupta57?lang=de - https://www.linkedin.com/posts/richard-zhuang-a4617226b_are-chatgpt-and-gpt-4-good-poker-players-activity-7272158663857827853-9tVJ - https://www.linkedin.com/posts/akshat57_are-chatgpt-and-gpt-4-good-poker-players-activity-7272156083509428224-eyQx - https://www.chatpaper.com/chatpaper/fr?id=3&date=1736870400&page=1 - https://ar5iv.labs.arxiv.org/html/2308.12466 - https://arxiv.org/abs/2401.06781 - https://www.amazon.de/Become-Successful-Professional-Poker-Player/dp/1461048184 - https://www.youtube.com/watch?v=MWRXx2saLw4 - https://medium.com/@JonathanLittle1/should-you-try-to-become-a-professional-poker-player-9723b3602991 - https://www.reddit.com/r/poker/comments/zq5t94/how_hard_is_it_really_to_become_a_pro_in_poker/ ```