Human-in-the-Loop Annotation

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Human-in-the-loop annotation studies how people and AI can work together to label data for training, evaluation, and analysis. Common approaches use active learning to select informative examples, model-generated drafts or propagated labels for people to correct, and uncertainty-based routing to send difficult cases to experts. Tools and workflows span text, images, video, audio, and clinical data, while measurement methods examine annotator differences, disagreement, and label quality. A central tension is how to reduce time and cost without obscuring errors, bias, or genuine differences in judgment; automation still needs careful validation, especially on subjective and high-stakes tasks.

Latest papers

135 papers12 papers in the last four weeks, down 29% on the four weeks before. 0.1% of all new papers.

Mar 9, 2026

Sandpiper: Orchestrated AI-Annotation for Educational Discourse at Scale

Digital educational environments are expanding toward complex AI and human discourse, providing researchers with an abundance of data that offers deep insights into learning and instructional processes. However, traditional qualitative analysis remains a labor-intensive bottleneck, severely limiting the scale at which this research can be conducted. We present Sandpiper, a mixed-initiative system designed to serve as a bridge between high-volume conversational data and human qualitative expertise. By tightly coupling interactive researcher dashboards with agentic Large Language Model (LLM) engines, the platform enables scalable analysis without sacrificing methodological rigor. Sandpiper addresses critical barriers to AI adoption in education by implementing context-aware, automated de-identification workflows supported by secure, university-housed infrastructure to ensure data privacy. Furthermore, the system employs schema-constrained orchestration to eliminate LLM hallucinations and enforces strict adherence to qualitative codebooks. An integrated evaluations engine allows for the continuous benchmarking of AI performance against human labels, fostering an iterative approach to model refinement and validation. We propose a user study to evaluate the system's efficacy in improving research efficiency, inter-rater reliability, and researcher trust in AI-assisted qualitative workflows.

Daryl Hedley, Doug Pietrzak, Jorge Dias +9

National Tutoring Observatory · FreshCognate · Cornell University +2

LLM-Assisted Annotation Human-in-the-Loop Annotation Human-AI Collaboration Educational Data Mining

Mar 5, 2026

Evaluating and Correcting Human Annotation Bias in Dynamic Micro-Expression Recognition

Existing manual labeling of micro-expressions is subject to errors in accuracy, especially in cross-cultural scenarios where deviation in labeling of key frames is more prominent. To address this issue, this paper presents a novel Global Anti-Monotonic Differential Selection Strategy (GAMDSS) architecture for enhancing the effectiveness of spatio-temporal modeling of micro-expressions through keyframe re-selection. Specifically, the method identifies Onset and Apex frames, which are characterized by significant micro-expression variation, from complete micro-expression action sequences via a dynamic frame reselection mechanism. It then uses these to determine Offset frames and construct a rich spatio-temporal dynamic representation. A two-branch structure with shared parameters is then used to efficiently extract spatio-temporal features. Extensive experiments are conducted on seven widely recognized micro-expression datasets. The results demonstrate that GAMDSS effectively reduces subjective errors caused by human factors in multicultural datasets such as SAMM and 4DME. Furthermore, quantitative analyses confirm that offset-frame annotations in multicultural datasets are more uncertain, providing theoretical justification for standardizing micro-expression annotations. These findings directly support our argument for reconsidering the validity and generalizability of dataset annotation paradigms. Notably, this design can be integrated into existing models without increasing the number of parameters, offering a new approach to enhancing micro-expression recognition performance. The source code is available on GitHub[https://github.com/Cross-Innovation-Lab/GAMDSS].

Feng Liu, Bingyu Nan, Xuezhong Qian +1

School of Psychology, Shanghai Jiao Tong University, Shanghai 200030, China · School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi 214122, China

Micro-Expression Recognition Facial Expression Recognition Video Frame Selection Human-in-the-Loop Annotation

Mar 4, 2026

LabelBuddy: An Open Source Music and Audio Language Annotation Tagging Tool Using AI Assistance

The advancement of Machine learning (ML), Large Audio Language Models (LALMs), and autonomous AI agents in Music Information Retrieval (MIR) necessitates a shift from static tagging to rich, human-aligned representation learning. However, the scarcity of open-source infrastructure capable of capturing the subjective nuances of audio annotation remains a critical bottleneck. This paper introduces LabelBuddy, an open-source collaborative auto-tagging audio annotation tool designed to bridge the gap between human intent and machine understanding. Unlike static tools, it decouples the interface from inference via containerized backends, allowing users to plug in custom models for AI-assisted pre-annotation. We describe the system architecture, which supports multi-user consensus, containerized model isolation, and a roadmap for extending agents and LALMs. Code available at https://github.com/GiannisProkopiou/gsoc2022-Label-buddy.

Ioannis Prokopiou, Ioannis Sina, Agisilaos Kounelis +2

Athens University of Economics and Business · Orfium · University of Patras +1

Human-in-the-Loop Annotation Music Information Retrieval

Mar 2, 2026

AnnoABSA: A Web-Based Annotation Tool for Aspect-Based Sentiment Analysis with Retrieval-Augmented Suggestions

We introduce AnnoABSA, the first web-based annotation tool to support the full spectrum of Aspect-Based Sentiment Analysis (ABSA) tasks. The tool is highly customizable, enabling flexible configuration of sentiment elements and task-specific requirements. Alongside manual annotation, AnnoABSA provides optional Large Language Model (LLM)-based retrieval-augmented generation (RAG) suggestions that offer context-aware assistance in a human-in-the-loop approach, keeping the human annotator in control. To improve prediction quality over time, the system retrieves the ten most similar examples that are already annotated and adds them as few-shot examples in the prompt, ensuring that suggestions become increasingly accurate as the annotation process progresses. Released as open-source software under the MIT License, AnnoABSA is freely accessible and easily extendable for research and practical applications.

Nils Constantin Hellwig, Jakob Fehle, Udo Kruschwitz +1

Media Informatics Group, University of Regensburg, Regensburg, Germany · Information Science Group, University of Regensburg, Regensburg, Germany

Aspect-Based Sentiment Analysis LLM-Assisted Annotation Human-in-the-Loop Annotation

Feb 27, 2026

A Human-Centered Workflow for Using Large Language Models in Content Analysis

While many researchers use Large Language Models (LLMs) through chat-based access, their real potential lies in leveraging LLMs via application programming interfaces (APIs). This paper conceptualizes LLMs as universal text processing machines and presents a comprehensive workflow for employing LLMs in three qualitative and quantitative content analysis tasks: (1) annotation (an umbrella term for qualitative coding, labeling and text classification), (2) summarization, and (3) information extraction. The workflow is explicitly human-centered. Researchers design, supervise, and validate each stage of the LLM process to ensure rigor and transparency. Our approach synthesizes insights from extensive methodological literature across multiple disciplines: political science, sociology, computer science, psychology, and management. We outline validation procedures and best practices to address key limitations of LLMs, such as their black-box nature, prompt sensitivity, and tendency to hallucinate. To facilitate practical implementation, we provide supplementary materials, including a prompt library and Python code in Jupyter Notebook format, accompanied by detailed usage instructions.

Ivan Zupic

School of Creative Management Goldsmiths, University of London

LLM-Assisted Annotation Human-in-the-Loop Annotation LLM Information Extraction

Feb 26, 2026

Interactive Medical-SAM2 GUI: A Napari-based semi-automatic annotation tool for medical images

Interactive Medical-SAM2 GUI is an open-source desktop application for semi-automatic annotation of 2D and 3D medical images. Built on the Napari multi-dimensional viewer, box/point prompting is integrated with SAM2-style propagation by treating a 3D volume as a slice sequence, enabling mask propagation from sparse prompts using Medical-SAM2 on top of SAM2. Voxel-level annotation remains essential for developing and validating medical imaging algorithms, yet manual labeling is slow and expensive for 3D scans, and existing integrations frequently emphasize per-slice interaction without providing a unified, cohort-oriented workflow for navigation, propagation, interactive correction, and quantitative export in a single local pipeline. To address this practical limitation, a local-first Napari workflow is provided for efficient 3D annotation across multiple studies using standard DICOM series and/or NIfTI volumes. Users can annotate cases sequentially under a single root folder with explicit proceed/skip actions, initialize objects via box-first prompting (including first/last-slice initialization for single-object propagation), refine predictions with point prompts, and finalize labels through prompt-first correction prior to saving. During export, per-object volumetry and 3D volume rendering are supported, and image geometry is preserved via SimpleITK. The GUI is implemented in Python using Napari and PyTorch, with optional N4 bias-field correction, and is intended exclusively for research annotation workflows. The code is released on the project page: https://github.com/SKKU-IBE/Medical-SAM2GUI/.

Woojae Hong, Jong Ha Hwang, Jiyong Chung +3

Department of Biomechatronic Engineering, Sungkyunkwan University, Suwon, Gyeonggi, Republic of Korea · Department of Neurosurgery, Seoul National University Hospital, Seoul National University College of Medicine, Seoul, Republic of Korea

Interactive Medical Image Segmentation Human-in-the-Loop Annotation Medical Image Analysis 3D Medical Image Segmentation

Feb 25, 2026

Understanding Annotation Error Propagation and Learning an Adaptive Policy for Expert Intervention in Barrett's Video Segmentation

Accurate annotation of endoscopic videos is essential yet time-consuming, particularly for challenging datasets such as dysplasia in Barrett's esophagus, where the affected regions are irregular and lack clear boundaries. Semi-automatic tools like Segment Anything Model 2 (SAM2) can ease this process by propagating annotations across frames, but small errors often accumulate and reduce accuracy, requiring expert review and correction. To address this, we systematically study how annotation errors propagate across different prompt types, namely masks, boxes, and points, and propose Learning-to-Re-Prompt (L2RP), a cost-aware framework that learns when and where to seek expert input. By tuning a human-cost parameter, our method balances annotation effort and segmentation accuracy. Experiments on a private Barrett's dysplasia dataset and the public SUN-SEG benchmark demonstrate improved temporal consistency and superior performance over baseline strategies.

Lokesha Rasanjalee, Jin Lin Tan, Dileepa Pitawela +2

School of Computer Science and Information Technology, Adelaide University, Australia · The Australian Institute for Machine Learning (AIML), Australia · Adelaide Medical School, Faculty of Health and Medical Sciences, Adelaide University, Australia +1

Interactive Medical Image Segmentation Human-in-the-Loop Annotation Video Object Segmentation Medical Image Segmentation Promptable Image Segmentation

Feb 23, 2026

AuditoryHuM: Auditory Scene Label Generation and Clustering using Human-MLLM Collaboration

Manual annotation and clustering of audio datasets is labour intensive. We introduce AuditoryHuM, a training-free framework for the unsupervised discovery and clustering of auditory scene labels using human-Multimodal Large Language Model (MLLM) collaboration. Leveraging MLLMs, our framework generates contextually relevant labels for audio data. To ensure label quality and mitigate hallucinations, zero-shot learning (Human-CLAP) quantifies the alignment between generated text labels and raw audio. A targeted human-in-the-loop intervention, refines only the lowest aligned pairs. The discovered labels form an interpretable alignment vector to group audio into cohesive clusters. The framework was evaluated across three auditory scene datasets (ADVANCE, AHEAD-DS, and TAU 2019), achieving a 96.1% reduction in human labour for ADVANCE during testing. Downstream models trained on our clusters exhibit enhanced classification accuracy vs the baseline (0.79 to 0.84) in clean acoustic environments, though performance scales down in dense, complex soundscapes. The project page: https://github.com/Australian-Future-Hearing-Initiative

Henry Zhong, Jörg M. Buchholz, Julian Maclaren +2

Australian Hearing Hub, Macquarie University, Sydney, Australia · Google Research Australia, Sydney, Australia

Clustering LLM-Assisted Annotation Human-in-the-Loop Annotation Unsupervised Clustering Audio Understanding

Feb 17, 2026

Beyond Labels: Information-Efficient Human-in-the-Loop Learning using Ranking and Selection Queries

Integrating human expertise into machine learning systems often reduces the role of experts to labeling oracles, a paradigm that limits the amount of information exchanged and fails to capture the nuances of human judgment. We address this challenge by developing a human-in-the-loop framework to learn binary classifiers with rich query types, consisting of item ranking and exemplar selection. We first introduce probabilistic human response models for these rich queries motivated by the relationship experimentally observed between the perceived implicit score of an item and its distance to the unknown classifier. Using these models, we then design active learning algorithms that leverage the rich queries to increase the information gained per interaction. We provide theoretical bounds on sample complexity and develop a tractable and computationally efficient variational approximation. Through experiments with simulated annotators derived from crowdsourced word-sentiment and image-aesthetic datasets, we demonstrate significant reductions on sample complexity. We further extend active learning strategies to select queries that maximize information rate, explicitly balancing informational value against annotation cost. This algorithm in the word sentiment classification task reduces learning time by more than 57% compared to traditional label-only active learning.

Belén Martín-Urcelay, Yoonsang Lee, Matthieu R. Bloch +1

School of Electrical and Computer Engineering, Georgia Institute of Technology, Atlanta, GA 30332 USA.

Human-in-the-Loop Annotation Active Learning

Feb 17, 2026

CREMD: Crowd-Sourced Emotional Multimodal Dogs Dataset

Dog emotion recognition plays a crucial role in enhancing human-animal interactions, veterinary care, and the development of automated systems for monitoring canine well-being. However, accurately interpreting dog emotions is challenging due to the subjective nature of emotional assessments and the absence of standardized ground truth methods. We present the CREMD (Crowd-sourced Emotional Multimodal Dogs Dataset), a comprehensive dataset exploring how different presentation modes (e.g., context, audio, video) and annotator characteristics (e.g., dog ownership, gender, professional experience) influence the perception and labeling of dog emotions. The dataset consists of 923 video clips presented in three distinct modes: without context or audio, with context but no audio, and with both context and audio. We analyze annotations from diverse participants, including dog owners, professionals, and individuals with varying demographic backgrounds and experience levels, to identify factors that influence reliable dog emotion recognition. Our findings reveal several key insights: (1) while adding visual context significantly improved annotation agreement, our findings regarding audio cues are inconclusive due to design limitations (specifically, the absence of a no-context-with-audio condition and limited clean audio availability); (2) contrary to expectations, non-owners and male annotators showed higher agreement levels than dog owners and female annotators, respectively, while professionals showed higher agreement levels, aligned with our initial hypothesis; and (3) the presence of audio substantially increased annotators' confidence in identifying specific emotions, particularly anger and fear.

Jinho Baek, Houwei Cao, Kate Blackwell

dept. of Computer Science, New York Institute of Technology, New York, USA

Emotion Recognition Multimodal Emotion Recognition Annotator Disagreement Human-in-the-Loop Annotation

Dec 14, 2025

Active Learning with Imperfect Labels: Optimal Labeler Assignment and Sample Selection

Active Learning (AL) is commonly used in applications where labeling data is expensive or time-consuming. In practice, however, labels are often noisy due to varying labeler expertise and annotation uncertainty, especially for complex or ambiguous samples. Learning from such imperfectly labeled data can degrade classifier performance. We propose an AL framework that explicitly accounts for label noise by optimally assigning labelers and selecting samples to minimize labeling error. Our approach, called OLAS (Optimal Labeler Assignment and Sampling), uses a noise model that depends on both labeler accuracy and model uncertainty to guide these decisions. We develop two tractable optimization formulations: one for assigning samples to labelers to minimize worst-case noise, and another for selecting samples while controlling overall label noise. Theoretical results provide closed-form solutions under mild conditions. Empirical evaluations on benchmark datasets and a real-world warranty claim classification problem show that OLAS achieves the highest or near-highest classification accuracy among existing AL strategies across most settings, using only a single label per sample.

Pouya Ahadi, Blair Winograd, Camille Zaug +3

School of Industrial and Systems Engineering Georgia Institute of Technology Atlanta, GA 30332, USA · Ford Motor Company

Human-in-the-Loop Annotation Active Learning Learning with Noisy Labels

Nov 5, 2025

SiLVi: Simple Interface for Labeling Video Interactions

Computer vision methods are increasingly used for the automated analysis of large volumes of video data collected through camera traps, drones, or direct observations of animals in the wild. While recent advances have focused primarily on detecting individual actions, much less work has addressed the detection and annotation of interactions -- a crucial aspect for understanding social and individualized animal behavior. Existing open-source annotation tools support either behavioral labeling without localization of individuals, or localization without the capacity to capture interactions. To bridge this gap, we present SiLVi, an open-source labeling software that integrates both functionalities. SiLVi enables researchers to annotate behaviors and interactions directly within video data, generating structured outputs suitable for training and validating computer vision models. By linking behavioral ecology with computer vision, SiLVi facilitates the development of automated approaches for fine-grained behavioral analyses. Although developed primarily in the context of animal behavior, SiLVi could be useful more broadly to annotate human interactions in other videos that require extracting dynamic scene graphs. The software, along with documentation and download instructions, is available at: https://silvi.ozankanbertay.com.

Ozan Kanbertay, Richard Vogg, Elif Karakoc +3

Institute of Computer Science and Campus Institute Data Science, University of Göttingen · Behavioral Ecology & Sociobiology Unit, German Primate Center, Göttingen, Germany · Department of Sociobiology/Anthropology, University of Göttingen, Göttingen, Germany

Animal Behavior Analysis Human-in-the-Loop Annotation

Sep 22, 2025

Interactive In-Meeting Speaker Correction with Human Feedback

Most automatic speech processing systems operate in ``open loop'' mode without user feedback about who said what, yet human-in-the-loop workflows can potentially enable higher accuracy. We propose an LLM-assisted in-meeting speaker correction system that lets users fix speaker attribution errors through brief corrective feedback. After performing streaming ASR and diarization, the system presents concise LLM-generated summaries to help users identify important speaker errors, and it incorporates user feedback by updating the speaker-attributed transcript and adding online speaker enrollments. To make this workflow effective despite errors in speech processing, LLM analysis, and user feedback, we developed several mechanisms to identify the intended correction more precisely. Further, we built an LLM-driven user feedback simulation to evaluate the workflow reprodubilty and at scale. Applied to the AMI headset test set, our system substantially reduces the DER from a streaming baseline (Google ASR + ECAPA) by 31.99% and speaker substitution error by 52.68%. Results of a pilot usability study suggest several avenues to improve the user experience.

Xinlu He, Yiwen Guan, Badrivishal Paurana +3

Worcester Polytechnic Institute

LLM-Assisted Annotation Human-in-the-Loop Annotation Speaker Diarization Speaker-Attributed ASR

Feb 9, 2023

The Re-Label Method For Data-Centric Machine Learning

In industry deep learning application, our manually labeled data has a certain number of noisy data. To solve this problem and achieve more than 90 score in dev dataset, we present a simple method to find the noisy data and re-label the noisy data by human, given the model predictions as references in human labeling. In this paper, we illustrate our idea for a broad set of deep learning tasks, includes classification, sequence tagging, object detection, sequence generation, click-through rate prediction. The dev dataset evaluation results and human evaluation results verify our idea.

Tong Guo

Learning with Noisy Labels Human-in-the-Loop Annotation

Jan 30, 2021

Learning From How Humans Correct

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we need to relabel the noisy data in our dataset for our industry application. The experiment result shows that our learn-on-correction method improve the classification accuracy from 91.7% to 92.5% in test dataset. The 91.7% accuracy is trained on the corrected dataset, which improve the baseline from 83.3% to 91.7% in test dataset. The accuracy under human evaluation achieves more than 97%.

Tong Guo

Learning with Noisy Labels Training Data Curation Human-in-the-Loop Annotation Text Classification