Evaluating the Performance of Large Language Models (LLMs) Through Grid-Based Game Competitions: An Extensible Benchmark and Leaderboard on the Path to Artificial General Intelligence (AGI)

Year 2024, Volume: 9 Issue: 2, 8 - 19, 01.02.2025

Oguzhan Topsakal , Edell Colby Harper Jackson

https://doi.org/10.52876/jcs.1611181

Abstract

Grid-based games, such as Tic-Tac-Toe, Connect-Four, and Gomoku, offer a valuable platform for evaluating large language models (LLMs) in reasoning, rule comprehension, and strategic thinking which are key skills for advancing Artificial General Intelligence (AGI). Current evaluation benchmarks often focus on tasks like natural language understanding or domain-specific problem-solving, lacking in multi-step reasoning and decision-making assessments. This study introduces an extensible benchmark framework leveraging these games to evaluate LLMs using three prompt types: list, illustration, and image. The framework's modular design facilitates the addition of new games, dynamic rule changes, and advanced prompt engineering techniques, enabling deeper examination of LLM capabilities. Through 2,310 simulated matches, we evaluated leading LLMs, including Claude 3.5 Sonnet, GPT-4 Turbo, and Llama3-70B. Results revealed significant performance variations, with simpler games like Tic-Tac-Toe yielding fewer invalid moves, while more complex games like Connect-Four and Gomoku posed greater challenges. List prompts were generally well-handled, while illustration and image prompts led to higher rates of disqualifications and missed opportunities. The findings underscore the utility of grid-based games as benchmarks for evaluating strategic thinking and adaptability, with implications for robotics, autonomous systems, and interactive AI. Limitations in handling visual data and complex scenarios suggest areas for improvement. The open-source nature of the benchmark encourages transparency and community contributions, fostering collaborative advancements in LLM research. Future directions include expanding to more complex games, refining prompt techniques, and exploring dynamic rule changes to deepen insights into LLM reasoning capabilities. This study lays the groundwork for advancing AI evaluation through flexible and comprehensive benchmarking tools, guiding progress toward more sophisticated and real-world applications.

Keywords

AGI, Artificial General Intelligence, Benchmark, Large Language Model, LLM, Game, LLM Benchmarking, AI Evaluation, Prompt Engineering, Open-Source, Game Simulation, Strategic Thinking, Grid Games, Rule Comprehension

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