Summary of Gtbench: Uncovering the Strategic Reasoning Limitations Of Llms Via Game-theoretic Evaluations, by Jinhao Duan et al.
GTBench: Uncovering the Strategic Reasoning Limitations of LLMs via Game-Theoretic Evaluations
by Jinhao Duan, Renming Zhang, James Diffenderfer, Bhavya Kailkhura, Lichao Sun, Elias Stengel-Eskin, Mohit Bansal, Tianlong Chen, Kaidi Xu
First submitted to arxiv on: 19 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 Large Language Models (LLMs) have become essential in real-world applications, requiring not only language capabilities but also strategic and logical reasoning abilities. This study evaluates the reasoning abilities of LLMs in competitive environments through game-theoretic tasks. The researchers propose GTBench, a language-driven environment comprising 10 widely recognized tasks across various gaming scenarios. They then characterize the game-theoretic reasoning of LLMs, conduct competitions between different LLM models, and analyze their performance. Notably, commercial LLMs outperform open-source ones in complex games. The study also explores the impact of code-pretraining and advanced reasoning methods like Chain-of-Thought (CoT) and Tree-of-Thought (ToT) on strategic reasoning. The findings provide insights into the game-theoretic properties of LLMs, such as equilibrium and Pareto Efficiency. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models are really smart computers that can understand language, but they also need to be able to make good decisions and think strategically. This study looks at how well these models do in games where you have to think ahead and make clever moves. The researchers created a special environment with 10 different games that test the models’ strategic reasoning abilities. They found out that some models are better than others at certain types of games, and that commercial models are generally better than open-source ones. They also looked at how training these models to do specific tasks affects their ability to make good decisions. |
Keywords
* Artificial intelligence * Pretraining
