How to evaluate the reasoning capabilities of LLMs in a more dynamic scenario
As evaluating the reasoning abilities of large language models (LLMs) becomes more common, the actual evaluation process and methodology presents significant challenges, especially when relying on static datasets (eg psychometric tests or math problems) that may become outdated, incomplete or compromised. To address this, researchers from the University of California, San Diego, and the University of California, Berkeley, have developed GameArena, a dynamic benchmark that assesses LLM reasoning through interactive gameplay with humans.
In “GameArena: Evaluating LLM Reasoning Through Live Computer Games” (2024), by Lanxiang Hu, Qiyu Li, Anze Xie, et al., the authors introduce a suite of three games — Akinator, Taboo, and Bluffing — each designed to test specific reasoning capabilities such as deductive, inductive, abductive, and multi-hop reasoning. By embedding evaluation tasks into these engaging games, GameArena aims to provide a more accurate and enjoyable assessment of LLMs’ reasoning skills.
The researchers collected over 2,000 game sessions, analyzing the data to uncover the underlying reasoning processes of five state-of-the-art LLMs. Their findings offer detailed assessments of various reasoning capabilities, highlighting the strengths and weaknesses of each model. A user study involving 100 participants indicated that GameArena enhances user engagement compared to existing benchmarks like Chatbot Arena. Notably, GameArena enables the collection of step-by-step LLM reasoning data in real-world settings, providing valuable insights into model performance.
To me, this paper is interesting for its innovative approach to evaluating LLM reasoning in dynamic, interactive environments, moving beyond traditional static datasets. By integrating LLMs into familiar games that require complex reasoning, the study offers a more nuanced understanding of these models’ capabilities and limitations. Further extensions of such approach seem very promising when other aspects of cognition can be addressed.
How do you perceive the effectiveness of using interactive games like Akinator, Taboo, and Bluffing (or others) to evaluate the reasoning abilities of large language models? Could this approach lead to more robust AI systems?
Paper: https://arxiv.org/pdf/2412.06394
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