标题： Favi-Score：用于生成式人工智能评估中偏袒行为的自动化偏好评分测量方法 显示英文标题

标题： Favi-Score: A Measure for Favoritism in Automated Preference Ratings for Generative AI Evaluation

摘要： 生成式AI系统已经普及到各种模态，这使得评估这些模型的问题变得更加紧迫。 一种流行的方法是偏好评分，其中不同系统的生成输出展示给评估者，评估者选择他们的偏好。 近年来，该领域转向开发自动（训练过的）度量标准来评估生成的输出，这些度量标准可以用来自动创建偏好评分。 在本工作中，我们研究了度量标准本身的评估，目前这些度量标准依赖于测量与人类判断的相关性或计算符号准确率得分。 这些措施仅评估度量标准与人类评分的一致性程度。 然而，我们的研究表明，这并没有讲完整个故事。 大多数度量标准与人类系统评估存在分歧，这种分歧往往偏向于特定的文本生成系统，暴露出自动化度量标准中存在一定程度的偏袒。 本文引入了偏好度量中偏袒的正式定义，并推导出了Favi-分数，用于衡量这一现象。 特别是，我们展示了偏袒与最终系统排名中的错误密切相关。 因此，我们建议基于偏好的度量标准应在符号准确率得分和偏袒性两方面进行评估。 显示英文摘要

摘要： Generative AI systems have become ubiquitous for all kinds of modalities, which makes the issue of the evaluation of such models more pressing. One popular approach is preference ratings, where the generated outputs of different systems are shown to evaluators who choose their preferences. In recent years the field shifted towards the development of automated (trained) metrics to assess generated outputs, which can be used to create preference ratings automatically. In this work, we investigate the evaluation of the metrics themselves, which currently rely on measuring the correlation to human judgments or computing sign accuracy scores. These measures only assess how well the metric agrees with the human ratings. However, our research shows that this does not tell the whole story. Most metrics exhibit a disagreement with human system assessments which is often skewed in favor of particular text generation systems, exposing a degree of favoritism in automated metrics. This paper introduces a formal definition of favoritism in preference metrics, and derives the Favi-Score, which measures this phenomenon. In particular we show that favoritism is strongly related to errors in final system rankings. Thus, we propose that preference-based metrics ought to be evaluated on both sign accuracy scores and favoritism.