2024/09/01 by Jasper Dekoninck, Dekoninck, Jasper, Maximilian Baader +3 · 1 citation
Computer Science · Engineering · Materials Science · #Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Digital Rights Management and Security #FOS: Computer and information sciences #Manufacturing Process and Optimization #Nuclear Materials and Properties
paper · pdf · doi:10.48550/arxiv.2409.00696
openalex publication_date 2024/09/01 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.