GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations

Original Paper: https://arxiv.org/abs/2402.12348

By: Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu

Abstract:

As Large Language Models (LLMs) are integrated into critical real-world applications, their strategic and logical reasoning abilities are increasingly crucial. This paper evaluates LLMs' reasoning abilities in competitive environments through game-theoretic tasks, e.g., board and card games that require pure logic and strategic reasoning to compete with opponents. We first propose GTBench, a language-driven environment composing 10 widely-recognized tasks, across a comprehensive game taxonomy: complete versus incomplete information, dynamic versus static, and probabilistic versus deterministic scenarios. Then, we investigate two key problems: (1) Characterizing game-theoretic reasoning of LLMs; (2) LLM-vs-LLM competitions as reasoning evaluation. We observe that (1) LLMs have distinct behaviors regarding various gaming scenarios; for example, LLMs fail in complete and deterministic games yet they are competitive in probabilistic gaming scenarios; (2) Open-source LLMs, e.g., CodeLlama-34b-Instruct, are less competitive than commercial LLMs, e.g., GPT-4, in complex games. In addition, code-pretraining greatly benefits strategic reasoning, while advanced reasoning methods such as Chain-of-Thought (CoT) and Tree-of-Thought (ToT) do not always help. Detailed error profiles are also provided for a better understanding of LLMs' behavior.

Summary Notes

Enhancing Strategic Thinking in AI with GTBENCH

In the dynamic world of artificial intelligence, Large Language Models (LLMs) are making waves, especially in areas demanding not just raw computational power but also strategic thought and reasoning, like cybersecurity and finance.

Traditional methods of evaluating these models might not fully capture their strategic reasoning abilities. This is where GTBENCH, a new game-theoretic evaluation framework, comes into play, offering a fresh perspective on how we assess the strategic capabilities of LLMs.

GTBENCH: A Closer Look

GTBENCH is a groundbreaking framework that uses language-driven, game-theoretic tasks to test LLMs.

It includes a wide range of games, from classics like Tic-Tac-Toe to more complex ones like Kuhn Poker, each selected to challenge LLMs' strategic decision-making in various scenarios.

Key Highlights of GTBENCH

• Wide Range of Games: From simple to complex, ensuring a thorough assessment of strategic reasoning.
• Strategic Depth: Evaluates LLMs in both probabilistic and deterministic settings, offering insights into their strategic complexity.

Evaluating Strategic Reasoning in LLMs

The study introduces the Normalized Relative Advantage (NRA) metric to measure LLM performance, comparing them with traditional game-solving strategies.

It tests both open-source models and commercial behemoths like GPT-4, providing a panoramic view of their strategic reasoning prowess.

Insights Gained

• Game Performance: LLMs perform better in probabilistic scenarios than in deterministic ones.
• Model Comparison: A notable difference in performance between commercial and open-source models, with code pretraining improving strategic reasoning.

Delving into Strategic Reasoning

This research goes deep into analyzing LLMs' strategic reasoning, examining their decision-making and negotiation strategies. It offers a window into how LLMs handle strategic scenarios and identifies areas for improvement.

Contributions and Looking Forward

GTBENCH represents a significant leap in our understanding and enhancement of LLMs' strategic reasoning skills. It not only highlights their strengths and weaknesses but also sets the stage for future research to further refine these capabilities.

Conclusion: Advancing LLMs' Strategic Reasoning

GTBENCH lays the groundwork for future advancements in LLMs' strategic reasoning. It's a crucial tool for AI engineers and researchers aiming to unlock LLMs' full potential in complex decision-making scenarios.

This framework not only showcases current capabilities but also outlines a path for future enhancements, promising to revolutionize decision-making in critical sectors with AI.

In summary, GTBENCH is a pivotal development in evaluating and improving the strategic reasoning of LLMs, marking a new direction for research and development in AI.