AI Leaderboard Illusion: Chatbot Arena Issues

AI leaderboards like Chatbot Arena suffer from issues like private testing, data asymmetry, and overfitting, hindering genuine progress and transparency.

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1. Leaderboard Distortion: The Chatbot Arena leaderboard, a popular ranking system for AI systems, suffers from systematic issues that distort the true playing field. This undermines the validity of the benchmark.

2. Private Testing Advantage: Certain providers, particularly those with closed models, have an unfair advantage due to undisclosed private testing practices. They can test multiple variants and retract scores, leading to biased results.

3. Data Access Asymmetry: Proprietary closed models receive disproportionately more data (battle samples) and are removed less often than open-weight models. This data access imbalance further skews the leaderboard results.

4. Overfitting to Arena Dynamics: The dynamics mentioned above (private testing, data asymmetry) lead to models overfitting to the specific characteristics of the Chatbot Arena, rather than developing generalizable AI capabilities.

5. Recommendations for Reform: The paper acknowledges the Arena's value but emphasizes the need for actionable recommendations to reform the evaluation framework, promoting fairer and more transparent benchmarking in AI.