Evaluating Reasoning-Based Scaffolds for Human-AI Co-Annotation: The ReasonAlign Annotation Protocol

2026-03-22

AI Summary

This study addresses the high variability in human annotations for subjective natural language processing tasks, which often arises from semantic ambiguity, and investigates the unclear impact of large language model (LLM)-generated reasoning on human annotation behavior. The authors propose ReasonAlign, a two-round Delphi-style annotation protocol that exposes annotators solely to LLM-generated rationales while withholding predicted labels, thereby isolating the effect of explanatory reasoning on inter-annotator agreement and label revision. Introducing AEP (Annotator Effort Proxy) as a novel metric to quantify the extent of annotation revisions, experiments on sentiment classification and opinion detection demonstrate that exposure to LLM rationales significantly improves annotation consistency with only minimal label changes, suggesting that such reasoning primarily aids in resolving ambiguous cases.

Technology Category

• Natural Language Processing: Safety and Robustness
• Knowledge Representation and Reasoning: Argumentation
• Machine Learning: Large Multimodal Models (LMMs)

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation

Abstract

Human annotation is central to NLP evaluation, yet subjective tasks often exhibit substantial variability across annotators. While large language models (LLMs) can provide structured reasoning to support annotation, their influence on human annotation behavior remains unclear. We introduce ReasonAlign, a reasoning-based annotation scaffold that exposes LLM-generated explanations while withholding predicted labels. We frame this as a controlled study of how reasoning affects human annotation behavior, rather than a full evaluation of annotation accuracy. Using a two-pass protocol inspired by Delphi-style revision, annotators first label instances independently and then revise their decisions after viewing model-generated reasoning. We evaluate the approach on sentiment classification and opinion detection tasks, analyzing changes in inter-annotator agreement and revision behavior. To quantify these effects, we introduce the Annotator Effort Proxy (AEP), a metric capturing the proportion of labels revised after exposure to reasoning. Our results show that exposure to reasoning is associated with increased agreement alongside minimal revision, suggesting that reasoning primarily helps resolve ambiguous cases without inducing widespread changes. These findings provide insight into how reasoning explanations shape annotation consistency and highlight reasoning-based scaffolds as a practical mechanism for supporting human-AI annotation workflows.

Problem

• human annotation
• annotation variability
• reasoning-based scaffolds
• human-AI co-annotation
• inter-annotator agreement

Innovation

• reasoning-based scaffolds
• human-AI co-annotation
• ReasonAlign
• Annotator Effort Proxy
• inter-annotator agreement