aiqbench analysis
Polyrating introduces a novel statistical framework using maximum a posteriori estimation to address critical flaws in current LLM evaluation methods, specifically high costs and inherent human biases. By leveraging existing benchmark scores, this approach significantly reduces the expense of human preference datasets while enabling more fair and cross-task comparisons of model performance. This work directly advances the field of machine learning by improving the reliability and efficiency of how we measure large language model capabilities.
Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation
arXiv:2409.00696v4 Announce Type: replace-cross Abstract: Rating-based human evaluation has become an essential tool to accurately evaluate the impressive performance of large language models (LLMs). However, current rating systems suffer from several important limitations: first, they fail to account for biases that significantly influence evaluation results, second, they require large and expensive preference datasets to obtain accurate ratings, and third, they do not facilitate meaningful comparisons of model ratings across different tasks. To address these issues, we introduce Polyrating, an expressive and flexible rating system based on maximum a posteriori estimation that enables a more nuanced and thorough analysis of model performance at lower costs. Polyrating can detect and quantify biases affecting human preferences, ensuring fairer model comparisons. Further, Polyrating can reduce the cost of human evaluations by up to $41\%$ for new models and up to $77\%$ for new tasks by leveraging existing benchmark scores. Lastly, Polyrating enables direct comparisons of ratings across different tasks, providing a comprehensive understanding of an LLMs' strengths, weaknesses, and relative performance across different applications.
Abstract:Rating-based human evaluation has become an essential tool to accurately evaluate the impressive performance of large language models (LLMs). However, current rating systems suffer from several important limitations: first, they fail to account for biases that significantly influence evaluation results, second, they require large and expensive preference datasets to obtain accurate ratings, and third, they do not facilitate meaningful comparisons of model ratings across different tasks. To address these issues, we introduce Polyrating, an expressive and flexible rating system based on maximum a posteriori estimation that enables a more nuanced and thorough analysis of model performance at lower costs. Polyrating can detect and quantify biases affecting human preferences, ensuring fairer model comparisons. Further, Polyrating can reduce the cost of human evaluations by up to $41\%$ for new models and up to $77\%$ for new tasks by leveraging existing benchmark scores. Lastly, Polyrating enables direct comparisons of ratings across different tasks, providing a comprehensive understanding of an LLMs' strengths, weaknesses, and relative performance across different applications.
Computation and Language (cs.CL); Artificial Intelligence (cs.AI)
arXiv:2409.00696 [cs.CL]
(or arXiv:2409.00696v4 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2409.00696 Focus to learn more arXiv-issued DOI via DataCite
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