annotation quality management

This study addresses the overreliance on inter-annotator agreement in current data annotation practices, which often overlooks annotation’s capacity to capture conceptual validity as a measurement act. Treating annotation as a measurement process, the work identifies five root causes of annotation issues—errors, ambiguity, impossibility, subjectivity, and annotator identity—and develops a measurement theory–based framework for diagnosing and improving annotation quality. Drawing on a synthesis of 132 literature sources and 10 semi-structured interviews, the research systematically defines target constructs, designs annotation instruments, implements labeling procedures, and evaluates both reliability and validity. The resulting framework equips annotation teams with evaluation methods that transcend mere agreement metrics, thereby substantially strengthening the foundational quality of AI training data.

NLP data quality assessment has long relied on inter-annotator agreement, overlooking intra-annotator consistency—the temporal stability of individual annotators’ judgments. This neglect challenges the implicit “gold label as ground truth” assumption. Method: We conduct exploratory repeated annotation experiments across major NLP datasets and quantify intra-annotator agreement using Cohen’s and Fleiss’ Kappa, complemented by qualitative perceptual analysis. Contribution/Results: We demonstrate that mainstream NLP datasets routinely omit intra-annotator consistency reporting; moreover, individual annotators exhibit significant temporal variability in labeling identical texts. We identify and disentangle the dual influence of textual ambiguity and subjectivity on annotation stability. Our work establishes intra-annotator agreement as a foundational data quality metric, providing both a methodological framework and concrete guidelines for constructing more robust, reproducible NLP datasets.

Amid the growing diversity of natural language processing tasks, existing inter-annotator agreement (IAA) metrics often suffer from limited applicability and interpretability when confronted with heterogeneous task types, label imbalance, and missing data. This work systematically reviews the theoretical foundations and practical methodologies of IAA, offering the first structured integration of mainstream metrics—such as Cohen’s Kappa and Krippendorff’s Alpha—organized by task type. It clarifies their underlying assumptions and delineates their boundaries of applicability. Furthermore, the study proposes a reliability assessment strategy that combines confidence intervals with analysis of disagreement patterns. By providing a clear, principled guide for selecting IAA metrics, this research significantly enhances the transparency, reproducibility, and scientific rigor of human annotation and evaluation practices in the NLP community.

Traditional AI-based educational systems rely on manual annotation and inter-rater reliability (IRR) to assess annotation quality—practices prone to human bias and insufficient for ensuring pedagogical validity. This paper critically redefines “annotation quality” around predictive validity: specifically, the capacity to forecast learning outcomes and support effective instructional interventions—shifting from static human consensus to a dynamic, closed-loop validity paradigm. We propose five novel validation methods: multi-label annotation, domain-expert judgment, cross-category consistency checks, learning-outcome association analysis, and closed-loop pedagogical experimentation. Empirical evaluation demonstrates that our framework substantially enhances the external validity of annotated data and the pedagogical actionability of AI models, enabling interpretable, intervention-ready learning insights. The work establishes both theoretical foundations and practical guidelines for developing scalable, educationally meaningful intelligent tutoring systems. (149 words)

This study investigates whether large language models (LLMs) can reliably replace human annotators to mitigate the high cost and logistical complexity of human-subject studies in software engineering innovation evaluation. We systematically evaluate six state-of-the-art LLMs across ten code-related annotation tasks—including code summary quality assessment and defect repair judgment—using five public datasets. Methodologically, we propose *inter-model agreement* as a novel task-adaptability predictor and integrate confidence-threshold filtering to identify samples safe for LLM-only annotation, thereby establishing a hybrid human–LLM evaluation paradigm. Results show that LLMs achieve or approach human inter-annotator agreement (Krippendorff’s α ≥ 0.8) on multiple tasks; inter-model agreement strongly predicts task feasibility (AUC = 0.92); and confidence-based filtering raises replacement accuracy to 94.3%.

This study addresses the widespread problem of incomplete reporting of annotation practices in natural language processing (NLP) research, which undermines reproducibility and quality assessment. Analyzing 1,603 papers from major NLP conferences between 2018 and 2025, the work introduces a unified taxonomy for annotation reporting that spans tasks, time, and domains, along with a minimal reporting standard. Leveraging a gold-standard dataset—Annotated-gold—curated through a combination of large language models and human adjudication, the authors construct Annotated-llm, achieving human-level inter-annotator agreement (Krippendorff’s α = 0.606) on structured information extraction. Despite gradual improvements in reporting over time, critical details—such as annotator training, linguistic competence, and compensation—remain frequently omitted. These findings advance the push toward more transparent and reliable annotation practices in NLP.

This study addresses a critical gap in machine learning education: the overreliance on pre-labeled datasets, which often obscures the subjectivity and ambiguity inherent in data annotation, leading students to place undue trust in model outputs. To counter this, the authors introduce an innovative pedagogical intervention that transforms manual annotation into an active learning tool. Students annotated hair coverage in skin lesion images using a three-point scale, followed by structured reflections via questionnaires. A cross-institutional experiment involving 43 participants from Fontys University of Applied Sciences (Netherlands) and the IT University of Copenhagen (Denmark) demonstrated that this approach significantly enhanced learners’ awareness of annotation ambiguity, dataset biases, and model limitations. Most participants acknowledged the influence of personal interpretation on labeling decisions and reported higher engagement compared to traditional instruction. This work provides the first empirical evidence supporting subjective annotation as an effective strategy for cultivating critical thinking about AI systems.

This work addresses the challenge that large language models (LLMs) struggle to adhere to domain-specific gold-standard annotation guidelines in zero-shot settings. To mitigate this limitation, the authors propose a mediation framework that iteratively reuses and refines annotation guidelines, introducing guideline evolution as a novel alignment mechanism to enhance annotation consistency and accuracy under low-supervision conditions. The approach integrates reasoning-optimized variants from three major LLM families—GPT, Gemini, and DeepSeek—and employs iterative guideline consolidation and fine-tuning. Evaluated on biomedical named entity recognition benchmarks including NCBI Disease, BC5CDR, and BioRED, the method demonstrates significant improvements in the models’ compliance with expert annotation standards.

This study addresses the inconsistent metric selection and inadequate reporting practices in LLM-as-judge research, which hinder reproducibility and cross-study comparison. Through a systematic analysis of agreement measures between large language models and human evaluators, the work reveals mathematical equivalences among multiple correlation coefficients—such as Pearson, Spearman, phi, and Matthews—under binary scoring, clarifies the distinct utility of Cohen’s κ, and elucidates how handling abstentions fundamentally affects evaluation outcomes. Building on these statistical insights, the paper proposes a standardized reporting checklist that encompasses rating scales, treatment of abstentions and ties, coverage, confusion matrices, and aggregation levels. This framework substantially enhances the transparency, comparability, and reproducibility of LLM-as-judge evaluations.