Bibliographic Information: Dekoninck, J., Baader, M., & Vechev, M. (2024). Polyrating: A Cost-Effective and Bias-Aware Rating System for LLM Evaluation. arXiv preprint arXiv:2409.00696v2.
Research Objective: This paper introduces Polyrating, a new rating system designed to overcome limitations of existing LLM evaluation methods, particularly concerning bias detection, sample efficiency, and cross-task comparability.
Methodology: Polyrating leverages maximum a posteriori (MAP) estimation to fit a linear model that incorporates both shared and model-specific features. Shared features capture judge biases, while model-specific features represent task-specific performance. The system utilizes existing data from LLM-based evaluations, traditional benchmarks, and human preference datasets to enhance sample efficiency.
Key Findings: Polyrating effectively quantifies the influence of various biases, including length, position, formality, sentiment, repetitiveness, and readability, on both human and LLM-based judgments. The system demonstrates significant improvements in sample efficiency, reducing the cost of human evaluation by up to 77% for new tasks and 41% for new models. Furthermore, Polyrating enables the creation of a multidimensional leaderboard that facilitates direct comparisons of LLM performance across different tasks.
Main Conclusions: Polyrating offers a more nuanced, robust, and cost-effective approach to LLM evaluation compared to traditional methods. By addressing key limitations related to bias, sample efficiency, and cross-task comparability, Polyrating provides a valuable tool for researchers and practitioners to better understand and compare the capabilities of different LLMs.
Significance: This research significantly contributes to the field of LLM evaluation by introducing a more sophisticated and practical rating system. Polyrating's ability to detect and quantify biases is crucial for ensuring fair model comparisons, while its improved sample efficiency makes human evaluation more feasible for resource-constrained settings.
Limitations and Future Research: While Polyrating offers a significant advancement, it relies on manual feature engineering, which can be time-consuming. Future research could explore automated methods for identifying relevant features and biases. Additionally, investigating the generalizability of Polyrating to other domains beyond LLM evaluation would be beneficial.
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by Jasper Dekon... lúc arxiv.org 10-15-2024
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