ReasonScaffold: A Scaffolded Reasoning-based Annotation Protocol for Human-AI Co-Annotation

• URL: http://arxiv.org/abs/2603.21094v2
• Date: Thu, 26 Mar 2026 06:53:43 GMT
• Title: ReasonScaffold: A Scaffolded Reasoning-based Annotation Protocol for Human-AI Co-Annotation
• Abstract summary: We introduce textbfReasonScaffold, a scaffolded reasoning annotation protocol that exposes LLM-generated explanations while withholding predicted labels.<n>We study how reasoning affects human annotation behavior in a controlled setting, rather than evaluating annotation accuracy.<n>Our results show that exposure to reasoning is associated with increased agreement, along with minimal revision, suggesting that reasoning helps resolve ambiguous cases without inducing widespread changes.
• Score: 2.5819252531158683
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• 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 underexplored. We introduce \textbf{ReasonScaffold}, a scaffolded reasoning annotation protocol that exposes LLM-generated explanations while withholding predicted labels. We study how reasoning affects human annotation behavior in a controlled setting, rather than evaluating 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, along with minimal revision, suggesting that reasoning 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 human--AI co-annotation workflows.

Related papers

• Data Annotation as Measurement [5.058544031889297]
  We argue that data annotation should be understood as a measurement problem.<n>Like other forms of measurement, annotation requires defining a concept, operationalizing it through an instrument, and evaluating the reliability and validity of the resulting measurements.
  arXiv  Detail & Related papers  (2026-08-07T14:51:27Z)
• A Resource for Enthymeme Detection in Controversial Political Discourse [0.42970700836450487]
  We present a resource of 1,482 tweets annotated by five annotators for the presence of enthymemes and their argument structure.<n>We propose annotation guidelines anchored in Walton's argumentation schemes, offering a structured and constrained approach.<n>Preliminary experiments show that models trained on annotator disagreement outperform models trained on hard majority-vote labels.
  arXiv  Detail & Related papers  (2026-06-10T15:09:36Z)
• Human Label Variation as Stable Signal: Learning Annotator-Specific Explanation Behavior via Cross-Annotator Preference Optimization [51.93456979139756]
  Free-text explanations extend human label variation beyond label disagreement.<n>We study whether large language models can learn and reproduce such annotator-specific label-explanation behavior.
  arXiv  Detail & Related papers  (2026-05-27T17:55:00Z)
• Quantifying the Statistical Effect of Rubric Modifications on Human-Autorater Agreement [50.34437999083224]
  Autoraters are increasingly used for evaluation and automated moderation content.<n>There is limited statistical analysis of how modifications in a rubric presented to both humans and autoraters affect their score agreement.
  arXiv  Detail & Related papers  (2026-05-07T13:55:11Z)
• Beyond Consensus: Perspectivist Modeling and Evaluation of Annotator Disagreement in NLP [25.097081181685613]
  Annotator disagreement is widespread in NLP, particularly for subjective and ambiguous tasks such as toxicity detection and stance analysis.<n>We first present a domain-agnostic taxonomy of the sources of disagreement spanning data, task, and annotator factors.<n>We then synthesize modeling approaches using a common framework defined by prediction targets and pooling structure.
  arXiv  Detail & Related papers  (2026-01-14T01:26:29Z)
• Can Reasoning Help Large Language Models Capture Human Annotator Disagreement? [84.32752330104775]
  Variation in human annotation (i.e., disagreements) is common in NLP.<n>We evaluate the influence of different reasoning settings on Large Language Model disagreement modeling.<n>Surprisingly, our results show that RLVR-style reasoning degrades performance in disagreement modeling.
  arXiv  Detail & Related papers  (2025-06-24T09:49:26Z)
• Regressor-Segmenter Mutual Prompt Learning for Crowd Counting [70.49246560246736]
  We propose mutual prompt learning (mPrompt) to solve bias and inaccuracy caused by annotation variance. Experiments show that mPrompt significantly reduces the Mean Average Error (MAE)
  arXiv  Detail & Related papers  (2023-12-04T07:53:59Z)
• Counterfactuals of Counterfactuals: a back-translation-inspired approach to analyse counterfactual editors [3.4253416336476246]
  We focus on the analysis of counterfactual, contrastive explanations. We propose a new back translation-inspired evaluation methodology. We show that by iteratively feeding the counterfactual to the explainer we can obtain valuable insights into the behaviour of both the predictor and the explainer models.
  arXiv  Detail & Related papers  (2023-05-26T16:04:28Z)
• Using Natural Language Explanations to Rescale Human Judgments [81.66697572357477]
  We propose a method to rescale ordinal annotations and explanations using large language models (LLMs)<n>We feed annotators' Likert ratings and corresponding explanations into an LLM and prompt it to produce a numeric score anchored in a scoring rubric.<n>Our method rescales the raw judgments without impacting agreement and brings the scores closer to human judgments grounded in the same scoring rubric.
  arXiv  Detail & Related papers  (2023-05-24T06:19:14Z)
• Logical Satisfiability of Counterfactuals for Faithful Explanations in NLI [60.142926537264714]
  We introduce the methodology of Faithfulness-through-Counterfactuals. It generates a counterfactual hypothesis based on the logical predicates expressed in the explanation. It then evaluates if the model's prediction on the counterfactual is consistent with that expressed logic.
  arXiv  Detail & Related papers  (2022-05-25T03:40:59Z)
• Evaluations and Methods for Explanation through Robustness Analysis [117.7235152610957]
  We establish a novel set of evaluation criteria for such feature based explanations by analysis. We obtain new explanations that are loosely necessary and sufficient for a prediction. We extend the explanation to extract the set of features that would move the current prediction to a target class.
  arXiv  Detail & Related papers  (2020-05-31T05:52:05Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.