HERO: Improving the Reliability and Sensitivity of Generative Model Evaluation Using Historical Data
HERO：利用历史数据提高生成模型评估的可靠性与敏感度AI 解读 ↓
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HERO：利用历史数据提高生成模型评估的可靠性与敏感度
- 研究问题
- 当专家标签稀少而普通标注噪声较大时，如何利用过去评估轮次的数据减少当前模型性能估计的偏差与方差？
- 方法与思路
- 作者利用历史专家标签校准普通标注者表现，再用历史数据中高精度测量的协变量稳定估计，并允许部分标注者跨轮次缺席。
- 材料陈述的贡献
- 摘要称 HERO 可用于多类评估任务，并建立偏差与方差下降的条件，通过模拟和真实模型评估基准展示方法表现。
- 阅读关注点
- 应核对历史与当前轮次的可迁移条件、标注者校准模型、协变量锚定方式，以及标注者重叠不足或领域变化时的有效性。
- 阅读边界
- 这是 arXiv 预印本，同行评审与期刊接收未核验；摘要未说明具体迁移条件或改进幅度，不能保证任意历史数据均有益。
原文摘要
Reliable generative AI models critically rely on expert human annotations to evaluate output quality, yet these "gold" labels are expensive to collect and limited in quantity. Organizations thus often turn to collecting vast but noisy "silver" labels from crowdsourced workers or vendor annotators as proxies for gold labels. Because gold remains the evaluation target, naively aggregating noisy silver labels may introduce bias, and estimators built on sparsely observed gold labels may have high variance to resolve the model performance gaps that guide practical decisions. Model evaluation has become an ongoing operational practice rather than a one-time exercise, with evaluation rounds repeating across model versions, releases, and content domains. A natural question is whether the previous historical evaluation data can be used to improve each new round of evaluation. We introduce HERO (History Enhanced RObust model evaluation), a novel framework that uses historical data to suppress bias (improve reliability) and reduce variance (improve sensitivity) in model performance evaluation. HERO calibrates silver labelers' performance learned from historical gold annotations, and stabilizes the resulting estimator by anchoring it to covariate information measured with high precision in the historical data. HERO can be broadly applied across multiple common evaluation tasks, and remains valid when only a subset of historical labelers appears in the current round. We establish conditions under which the bias and variance reductions hold, showcase HERO's performance in simulation studies, and demonstrate its effectiveness on real-world model evaluation benchmarking datasets.