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K-Sort Arena: Efficient and Reliable Benchmarking for Generative Models

The rapid development of visual generative models requires efficient and reliable evaluation methods. Arena platforms, which gather user votes on model comparisons, can rank models according to human preferences. However, traditional arena methods require excessive comparisons for ranking to converge and are vulnerable to preference noise.

This paper introduces K-Sort Arena, a new benchmarking platform that addresses the limitations of traditional arena platforms. K-Sort Arena is built on the key insight that images and videos possess higher perceptual intuitiveness than text. This allows for the rapid evaluation of multiple samples simultaneously.

K-Sort Arena uses K-wise comparisons, allowing K models to participate in free-for-all competitions. This provides richer information than pairwise comparisons. For example, if you are comparing four models, you can present all four outputs to the user and ask them to rank the models from best to worst. This is much more informative than asking the user to compare only two models at a time.

To improve the robustness of the platform, K-Sort Arena leverages probabilistic modeling and Bayesian updating techniques. This helps to reduce the impact of preference noise. For example, instead of simply assigning a numerical score to each model, K-Sort Arena represents the capability of each model as a normal distribution. This allows for a more nuanced understanding of the model’s performance and its uncertainty.

In addition, K-Sort Arena uses an exploration-exploitation based matchmaking strategy to facilitate more informative comparisons. This strategy aims to balance the need for exploring new models with the need for exploiting the knowledge gained from previous comparisons. For example, K-Sort Arena might prioritize comparing models that have similar capabilities to determine which one is truly better. This strategy also helps to ensure that all models are given a fair chance to be evaluated.

Overall, K-Sort Arena provides a number of advantages over traditional arena platforms. It is significantly faster, more reliable, and more robust to preference noise. The authors demonstrate these advantages through a series of experiments. For example, they show that K-Sort Arena converges 16.3x faster than the widely used ELO algorithm. They also show that K-Sort Arena is more robust to preference noise.

The authors have made K-Sort Arena available to the public as an open-source platform on Huggingface Space. This platform allows users to input prompts and receive outputs from K anonymous generative models. Users then vote on the outputs to help update the leaderboard. K-Sort Arena is a valuable resource for researchers and developers working in the field of visual generative models.

The paper concludes by discussing future work, including the integration of more advanced models and the development of a more comprehensive leaderboard. It is clear that K-Sort Arena is a significant advancement in the field of benchmarking for visual generative models. It is likely to have a major impact on the research and development of these models in the years to come.