Automated Data Annotation

Latest papers

23 papers5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

Maswao: An automatically collected, prepared, and benchmarked dataset for the South African Sign language
Nov 2, 2026Thapelo Sindane, Unarine Netshifhefhe, Vukosi Marivate +3Sign Language Datasets Sign Language Translation
Data Science For Social Impact Lab Department of Computer Science University of Pretoria · Lelapa AI · interdisciplinary center for digital futures Faculty of Economic and Management Sciences University of the Free State
Taurus: LLM-in-the-Loop CT Annotation Matches Coding Agents at Half the Cost
Oct 30, 2026Bing Hu, Rishabh Sharma, Jatin MehtaAutomated Data Annotation LLM-Assisted Annotation
David R. Cheriton School of Computer Science, University of Waterloo, Waterloo, Ontario, Canada
GAIDA: Generative AI for Human-in-the-Loop Document Annotation
Oct 19, 2026Islam Mesabah, Ali Elhosary, Priyabanta Sandulu +2Automated Data Annotation Human-in-the-Loop Annotation
German Research Center for Artificial Intelligence (DFKI) · RPTU Kaiserslautern-Landau, Kaiserslautern, Germany
Can This LLM Annotate My Data? Estimating Zero-Shot LLM Performance
Oct 16, 2026Tong Lin, Grant Schoenebeck, Sabina TomkinsLanguage Model Performance Prediction LLM-Assisted Annotation
School of Information, University of Michigan
CoDA: A Human-LM Collaborative Codebook Development Assistant for Domain-Specific Text Annotation
Oct 16, 2026Jaspreet Ranjit, Swaminathan Chellappa, Augusto Rivas Costante +5LLM-Assisted Annotation Automated Data Annotation
Thomas Lord Dept. of Computer Science, University of Southern California · My Friend’s Place, Los Angeles, California · School of Social Work, University of North Carolina
Goldsmith: Gold-Loss-Guided Definition Optimization with an Agentic Annotation Harness
Oct 7, 2026Yihan Li, Hanyi Zhang, Xiaoxi Jiang +1Automated Data Annotation LLM-Assisted Annotation
Sun Yat-sen University, Guangzhou, China · South China University of Technology, Guangzhou, China
QUORUM: QUality-Optimized Routing Using Multiple annotators
Aug 28, 2026Antonio Purificato, Maria Sofia Bucarelli, Andrea Bacciu +2Automated Data Annotation LLM-Assisted Annotation
Amazon · Sapienza University of Rome · Université Côte d’Azur, CNRS, Inria, I3S
TreeAgent: A Generalizable Multi-Agent Framework for Automated Bias Labeling in Forestry via Compiled Expert Rules and Vision-Language Models
Jun 30, 2026Shiyi Chen, Nicholas Saban, Collin Hargreaves +1Automated Data Annotation Interpretable ML
University of California, Berkeley, California, United States.
Learning to Annotate Delayed and False AEB Events: A Practical System for Extreme Class Imbalance and Asymmetric Label Noise
Jun 17, 2026Mengxiang Hao, Xin Jiang, Xinghao Huang +10Class-Imbalanced Learning Automated Data Annotation
All authors are with Li Auto, China.
TalkTag: Fine-Grained Morphosyntactic Error Annotation for Transcribed Speech
Jun 1, 2026Shamira Venturini, Oliver Hennhöfer, Steffen Kinkel +1LLM Fine-Tuning LLM-Assisted Annotation
Karlsruhe Institute of Technology · Karlsruhe University of Applied Sciences · Karlsruhe Institute of Technology, Germany +1
EvoPool: Evolutionary Programmatic Annotation for Label-Efficient Specialized Supervision
Jun 1, 2026Tianyi Xu, Yaolun Zhang, Xuan Ouyang +1Multi-Agent LLM Systems LLM-Assisted Annotation
Oregon State University · University of Wisconsin–Madison
Double Triangle Annotation: A Scalable Human-in-the-Loop Framework for High-Precision Historical Document Annotation
May 25, 2026Yi RenHuman-in-the-Loop Annotation Historical Document Processing
École Polytechnique Fédérale de Lausanne
From Model Uncertainty to Human Attention: Localization-Aware Visual Cues for Scalable Annotation Review
May 12, 2026Moussa Kassem Sbeyti, Joshua Holstein, Philipp Spitzer +2Uncertainty Quantification Automated Data Annotation
1Scientific Computing Center, Karlsruhe Institute of Technology. · Institute for Information Systems, Karlsruhe Institute of Technology.
Exploring and Testing Skill-Based Behavioral Profile Annotation: Human Operability and LLM Feasibility under Schema-Guided Execution
Apr 16, 2026Yufeng WuLLM-Assisted Annotation Human-in-the-Loop Annotation
City University of Hong Kong
Lifting Unlabeled Internet-level Data for 3D Scene Understanding
Apr 2, 2026Yixin Chen, Yaowei Zhang, Huangyue Yu +9Automated Data Annotation 3D Scene Understanding
State Key Laboratory of General Artificial Intelligence, BIGAI · Beijing University of Posts and Telecommunications · Peking University +2
More Human, More Efficient: Aligning Annotations with Quantized SLMs
Apr 1, 2026Jiayu Wang, Junyoung LeeLLM-Assisted Annotation Automated Data Annotation
Home Team Science and Technology Agency
Annotation-Free Detection of Drivable Areas and Curbs Leveraging LiDAR Point Cloud Maps
Mar 29, 2026Fulong Ma, Daojie Peng, Jun MaAutomated Data Annotation Autonomous Driving Perception
The Hong Kong University of Science and Technology (Guangzhou), Guangzhou 511453, China
GUIDE: Resolving Domain Bias in GUI Agents through Real-Time Web Video Retrieval and Plug-and-Play Annotation
Mar 27, 2026Rui Xie, Zhi Gao, Chenrui Shi +3Multimodal RAG Automated Data Annotation
Shanghai Jiao Tong University, Shanghai, China · State Key Laboratory for General Artificial Intelligence, BIGAI, Beijing, China · Beijing Institute of Technology, Beijing, China
Multi-Perspective LLM Annotations for Valid Analyses in Subjective Tasks
Mar 22, 2026Navya Mehrotra, Adam Visokay, Kristina GligorićAnnotator Disagreement LLM-Assisted Annotation
University of Washington
Balalaika: Data-Centric, Prosody-Aware Annotation Pipeline for Russian Speech
Jul 17, 2025Kirill Borodin, Nikita Vasiliev, Vasiliy Kudryavtsev +3Speech Processing Speech Prosody
lab260, Moscow Technical University of Communications and Informatics, Russia · BitmanagerAI, UAE
AutoExpert: Automating 3D LiDAR Annotation from Expert-Crafted Guidelines
Jun 3, 2025Yechi Ma, Wei Hua, Shu KongAutonomous Driving Datasets Lidar-Based 3D Object Detection
Zhejiang University · University of Macau · IAIBS
"You are an expert annotator": Automatic Best-Worst-Scaling Annotations for Emotion Intensity Modeling
Mar 26, 2024Christopher Bagdon, Prathamesh Karmalker, Harsha Gurulingappa +1LLM-Assisted Annotation Emotion Recognition
Merck Data & AI Organization, Merck Group, Darmstadt, Germany · Institut für Maschinelle Sprachverarbeitung, University of Stuttgart, Germany · Fundamentals of Natural Language Processing, University of Bamberg, Germany +1
GAnnET: A principled approach to automatically annotating charge stability diagrams
Dec 18, 2023Brian Weber, Justyna P. ZwolakAutomated Data Annotation Physics-Informed ML
Intelligent Geometries, LLC, Lake Frederick, VA 22630, USA · National Institute of Standards and Technology, Gaithersburg, MD 20899, USA · Joint Center for Quantum Information and Computer Science, University of Maryland, College Park, MD 20742, USA

Nov 2, 2026J/K move · Enter open · S save

Maswao: An automatically collected, prepared, and benchmarked dataset for the South African Sign language

Thapelo Sindane, Unarine Netshifhefhe, Vukosi Marivate, Abiodun Modupe+2

Data Science For Social Impact Lab Department of Computer Science University of Pretoria · Lelapa AI · interdisciplinary center for digital futures Faculty of Economic and Management Sciences University of the Free State

Sign Language Datasets Sign Language Translation Automated Data Annotation Low-Resource Language Processing

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Recent advancements in sign language processing can be attributed to developments in both large-scale dataset collection and algorithmic complexity in areas such as pose estimation techniques, signal processing through Transformer models, and model optimization. However, only a handful of sign languages, such as the American, British, and German, from over 200 globally recognized sign languages, reap the benefits of such advancements, while the rest lag behind. In this study, we aim to address this technological disparity by proposing a framework for the automatic collection and preparation of sign language datasets, with very minimal human-labour intervention, using the South African Sign Language (SASL) as a testbed. Specifically, we automatically collect SASL video content using public resources such as news broadcasts. Additionally, we convert their accompanying audio streams into text using available Automatic Speech Recognition (ASR) tools. Furthermore, we crop the sign language area in the videos and use gradient descent to locate valleys of non-motion to determine end-of-sentence points within long videos to create our weakly sentence-aligned parallel corpora -- the Maswao (Signs) dataset. Finally, we benchmark the weakly aligned dataset on the task of Sign-to-text Translation using Recurrent Neural Networks and Transformer models to compare their individual intricacies. Our results show that Transformer models proposed in the literature outperform recurrent baselines, with the best-performing Transformer achieving BLEU-1 to BLEU-4 scores of 13.78, 5.21, 2.52, and 1.54, respectively, as well as ChrF = 18.09 and ROUGE-L = 10.51. These results indicate that the task remains promising and warrants further investigations.