📄 中文摘要
在 AI 领域，基准测试的质量直接影响算法的评估和发展。研究表明，评估者的数量对结果的可靠性和一致性具有重要影响。通过分析不同数量评估者的评分数据，提出了一种优化评估者数量的方法，以提高基准测试的有效性和效率。此外，研究还探讨了评估者之间的偏差和一致性问题，强调了在设计基准测试时考虑评估者选择的重要性。最终，提出了一系列建议，以帮助研究人员和工程师在构建 AI 基准测试时做出更明智的决策。
构建更好的 AI 基准测试：多少评估者才够？
出处: Building better AI benchmarks: How many raters are enough?
发布: 2026年3月31日
📄 English Summary
Building better AI benchmarks: How many raters are enough?
The quality of benchmarks in the AI field directly impacts the evaluation and development of algorithms. Research indicates that the number of raters significantly affects the reliability and consistency of results. By analyzing scoring data from varying numbers of raters, a method to optimize the number of raters is proposed to enhance the effectiveness and efficiency of benchmarks. Additionally, the study addresses issues of bias and consistency among raters, emphasizing the importance of considering rater selection when designing benchmarks. A series of recommendations are ultimately provided to assist researchers and engineers in making more informed decisions when constructing AI benchmarks.