The rise of large language models (LLMs) has brought a critical need for high-quality human-labeled data, particularly for processes like human feedback and evaluation. A common practice is to label data via consensus annotation over crowdworker judgments. However, annotators' judgments for subjective tasks can differ in many ways: they may have different qualitative judgments about an example, and they may map those to a labeling scheme in different ways. We show that these nuances can be captured by natural language explanations, and propose a method to rescale ordinal annotations and explanations using LLMs. Specifically, we feed annotators' Likert ratings and corresponding explanations into an LLM and prompt it to produce a numeric score anchored in a scoring rubric. These scores should reflect the annotators' underlying assessments of the example. The rubric can be designed or modified after annotation, and include distinctions that may not have been known when the original error taxonomy was devised. We explore our technique in the context of rating system outputs for a document-grounded question answering task, where LLMs achieve near-human performance. Our method rescales the raw judgments without impacting agreement and brings the scores closer to human judgments grounded in the same scoring rubric.

翻译：大型语言模型（LLM）的兴起带来了对高质量人工标注数据的迫切需求，尤其是在人工反馈与评估等过程中。常见做法是通过众包标注者的共识标注对数据进行标注。然而，在主观性任务中，标注者的判断可能呈现多方面差异：他们对同一示例可能持有不同的定性判断，也可能以不同方式将这些判断映射到标注方案中。我们证明自然语言解释能够捕捉这些细微差异，并提出了一种利用LLM重新校准有序标注及解释的方法。具体而言，我们将标注者的李克特评分及相应解释输入LLM，并提示其按照评分标准生成数值分值。这些分值应反映标注者对示例的潜在评估。评分标准可在标注完成后设计或修改，并能包含原始错误分类法制定时未知的区分维度。我们在文档型问答任务的系统输出评分场景中验证了该技术，其中LLM已接近人类水平。该方法在不影响一致性的前提下重新校准原始评分，使分值更接近基于同一评分标准的人类判断。