Evaluating Reasoning-Based Scaffolds for Human-AI Co-Annotation: The ReasonAlign Annotation Protocol
Human annotation for subjective NLP tasks suffers from high inter-annotator disagreement. This paper introduces ReasonAlign, a protocol that exposes annotators to LLM-generated reasoning explanations (but not predicted labels) between two annotation passes. The goal is to test whether reasoning scaffolds improve annotation consistency without the anchoring bias typical of suggestion-based systems.
The paper presents a methodologically sound concept with the two-pass Delphi-style protocol and the novel AEP metric, but the empirical evaluation is too limited to support the strong conclusions. The jump from $\kappa=0.76$ to $\kappa=0.98$ on sentiment (with only 500 instances and 4 annotators) is eyebrow-raising, and the authors acknowledge they measure agreement, not correctness. The work is promising as a pilot study but needs substantially larger validation before practical adoption.
The core methodological insight is valuable: isolating reasoning from prediction to study its specific effect on annotation behavior. The Annotator Effort Proxy (AEP) is a sensible contribution, defined as $\mathrm{AEP}=\frac{\text{Number of revised labels}}{\text{Total labels}}$, quantifying collective annotator response to model explanations. The Delphi-inspired two-pass design controls for anticipation effects by keeping annotators blind to the second phase.
The sample size is critically small: 500 utterances with only four annotators per task. The near-perfect $\kappa=0.98$ post-reasoning on sentiment is suspicious and likely an artifact of the limited dataset combined with homogeneity in the conversational data. Critically, the paper conflates agreement with correctness: increased $\kappa$ could reflect shared bias induced by the LLM reasoning rather than improved understanding. The authors admit this in the Limitations: "increased agreement does not necessarily imply improved correctness; it remains possible that reasoning introduces shared biases across annotators." No direct measurement of anchoring effects was performed, and the claim that bidirectional revisions "is inconsistent with...anchoring" is weak evidence against anchoring.
The comparison to prior work is generally fair. The authors appropriately cite Schroeder et al. (2025) on LLM-assisted annotation risks and Chaleshtori et al. (2024) on inconsistent explanation utility. The distinction between ReasonAlign and prior suggestion-based systems is clearly articulated. However, the evidence only supports the narrow claim that reasoning exposure increases agreement in their specific setup—not that it produces better annotations. The bidirectional revision pattern (revisions in "both directions") is mentioned as evidence against anchoring but is not quantified. Without knowing the actual label distribution of revisions or comparison to a control condition where annotators re-label without seeing reasoning, causal claims are unsupported.
Reproducibility is seriously impaired. The paper states "Appendix A" describes the prompting procedure, but no supplementary material or code repository is linked or referenced. Critical hyperparameters like the LLM model (GPT-4? Llama? temperature settings?) are only partially disclosed—we know temperature=0.2 for consistency checks but not the main generation. The dataset source is not specified (synthetic? existing corpus?). The task guidelines given to annotators are not provided. Without the exact prompts, the self-example generation procedure, the reasoning templates, and the annotation interface, independent reproduction would require substantial guessing.
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.
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