Training Data Curation

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9 papers in the last four weeks, up 80% on the four weeks before. 0.1% of all new papers.

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Latest papers 104

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MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models
Jul 8, 2026Hyunjae Kim, Dain Kim, Pan Xiao +25Medical Imaging Foundation Models Training Data Curation
Yale University, New Haven, CT, USA · Korea University, Seoul, South Korea · Washington University in St. Louis, St. Louis, MO, USA +4
TriA Pipeline: A Large-Scale Automatic Audio Annotation Pipeline For Audio Classification In Specific Scenarios
Jul 7, 2026Hong Lyu, Mingru Yang, Qianhua He +3Training Data Curation Audio Understanding
School of Electronic and Information Engineering, South China University of Technology, Guangzhou, China
CurateEvo: Data-Curation Evolving for Agentic Post-Training
Jul 7, 2026Dingzirui Wang, Xuanliang Zhang, Keyan Xu +2Training Data Curation LLM Agent Training
Harbin Institue of Technology
BatteryLake: Agentic, Physics-Grounded Curation of Heterogeneous Battery Aging Data and Benchmarking
Jul 6, 2026Tianwen Zhu, Hao Wang, Yonggang WenRUL Estimation Benchmark Design
College of Computing and Data Science, Nanyang Technological University, Singapore
HERMES: A Multi-Granularity Labeling Substrate for Pre-training Data Mixtures
Jul 2, 2026Ziyun Qiao, Yue Min, Ruining Chen +1Language Model Pretraining Training Data Curation
Wizard Quant, Beijing, China · University of Science and Technology of China, Hefei, China
DataEvolver: Self-Evolving Multi-Agent Data Construction for Text-Rich Image Generation
Jun 30, 2026Siyu Yan, Yizhen Gao, Yilin Wang +2Synthetic Data Generation Training Data Curation
1Central South University
Revising RVL-CDIP: Quantifying Errors and Test-Train Overlap
Jun 30, 2026Stefan Larson, Attila Nagy, Sam Desai +8Data Leakage Training Data Curation
Vanderbilt University · ML Collective · University of Michigan +1
CORTEX: High-Quality Cross-Domain Organization of Web-Scale Corpora through Ontological Corpus Graph
Jun 29, 2026Chengtao Gan, Xiaoke Guo, Yushan Zhu +5KG Construction Training Data Curation
Zhejiang University · JIUTIAN Research, Beijing, China
Underwater Source Detection and Classification for Signal-based Surveillance: Audio Dataset Curation and Cross-Domain Evaluation
Jun 27, 2026Quoc Thinh Vo, David K. HanTraining Data Curation Audio Classification
Department of Electrical and Computer Engineering, Drexel University, Philadelphia, PA, USA
DataComp-VLM: Improved Open Datasets for Vision-Language Models
Jun 26, 2026Matteo Farina, Vishaal Udandarao, Thao Nguyen +33VLM Evaluation Training Data Curation
University of Trento · 2Tübingen AI Center, University of Tübingen · University of Cambridge +12
OpenThoughts-Agent: Data Recipes for Agentic Models
Jun 23, 2026Negin Raoof, Richard Zhuang, Marianna Nezhurina +47Training Data Curation LLM Agent Training
Data Selection Through Iterative Self-Filtering for Vision-Language Settings
Jun 22, 2026Andrei Liviu Nicolicioiu, Sarvjeet Singh Ghotra, Morgane M. Moss +1Vision-Language Models Training Data Curation
Mila, Université de Montréal
Large Language Model-Assisted Cleaning of Report-Derived Labels in a Large-Scale Chest CT Dataset
Jun 21, 2026Yosuke Yamagishi, Atsushi Takamatsu, Mototsugu Sato +4Training Data Curation LLM-Assisted Annotation
Division of Radiology and Biomedical Engineering, Graduate School of Medicine, The University of Tokyo, Tokyo, Japan · Department of Computational Diagnostic Radiology and Preventive Medicine, The University of Tokyo Hospital, Tokyo, Japan · Department of Radiology, Kanazawa University Hospital, Ishikawa, Japan +3
Learning from Audio-Dependency Errors: Data Curation Strategies Based on Model Confusion Patterns in Audio Question Answering
Jun 20, 2026Hyeonuk NamAudio-Language Model Evaluation Audio QA
Independent Researcher · Seoul, South Korea
Where Does the Signal Live? A Web Data Recipe for Medical Encoder Pretraining
Jun 20, 2026Bofeng Huang, Jacques Sun, Diane Bouchacourt +2Language Model Pretraining Masked Language Modeling
Doctolib · Work done while at Doctolib.
CuratorKIT : Data Curation and Synthetic Data Generation for LLM Post-Training
Jun 19, 2026Soham Bhattacharjee, Karun Sharma, Vinay Kumar Sankarapu +1Training Data Curation Synthetic Data Generation for Language Models
Lexsi Labs
DataClaw0: Agentic Tailoring Multimodal Data from Raw Streams
Jun 19, 2026Cong Wan, Zeyu Guo, Zijian Cai +6Training Data Curation Synthetic Data Generation for Language Models
Xi’an Jiaotong University · University of Chinese Academy of Sciences · Shenzhen University of Advanced Technology +1
Beyond Reward Engineering: A Data Recipe for Long-Context Reinforcement Learning
Jun 17, 2026Xiaoyue Xu, Sikui Zhang, Xiaorong Wang +2Training Data Curation RL for Language Model Reasoning
OpenBMB · Tsinghua University
Confusion-Aware Transfer Teacher Curriculum Learning Framework: Disentangling Scoring and Pacing Effects
Jun 16, 2026Savini Kommalage, Sanka Mohottala, Asiri Gawesha +5Training Data Curation Curriculum Learning
Faculty of Computing, Sri Lanka Institute of Information Technology, Sri Lanka · Faculty of Engineering, University of Sri Jayewardenepura, Sri Lanka · Faculty of Engineering, Sri Lanka Institute of Information Technology, Sri Lanka +2
DRIFT: Refining Instruction Data via On-Policy Data Attribution
Jun 16, 2026Zefan Wang, Lincheng Li, Tianyu Yu +1Supervised Fine-Tuning Training Data Curation
Tsinghua University
In-Domain Supervised Pathology Report Classification: A Reproducible Pipeline from Data Curation to Production-Matched Evaluation
Jun 14, 2026Isaac Hands, Bin Huang, Adam Spannaus +4Computational Pathology Training Data Curation
Department of Computer Science, University of Kentucky, Lexington, KY, USA · UK Markey Cancer Center, Lexington, KY, USA · Kentucky Cancer Registry, Lexington, KY, USA +3
Mask-Proof: An LLM-based Automated Data Curation Pipeline on Mathematical Proofs
Jun 13, 2026Jierui Zhang, Siyuan Tan, Xinhang Li +8Mathematical Reasoning Benchmarks LLM Evaluation
School of Computer Science Beijing University of Posts and Telecommunications Beijing, China · Graduate College for Engineers Beijing University of Posts and Telecommunications Beijing, China · School of Mathematical Sciences Fudan University Shanghai, China +6
Phase-Localized Curation Does Not Help: A Negative Result on Per-Phase Metric Selection for Demonstration Filtering
Jun 13, 2026Aarav BediTraining Data Curation
Department of Mechanical Engineering University of California, Berkeley
A Data-Centric Framework for Detecting and Correcting Corrupted Labels
Jun 10, 2026Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +3Training Data Curation Noisy-Label Learning
Noise-Aware Framework for Correcting Corrupted Labels
Jun 10, 2026Ha-Linh Nguyen, Hong-Anh Nguyen, Minh-Duc La +4Training Data Curation Noisy-Label Learning
Faculty of Information Technology, VNU University of Engineering and Technology, Hanoi, Vietnam
i1: A Simple and Fully Open Recipe for Strong Text-to-Image Models
Jun 9, 2026Boya Zeng, Tianze Luo, Shu Pu +4Training Data Curation T2I Generation
Princeton University
What Demonstration Curation Metrics Do to Your Policy
Jun 8, 2026Aarav BediTraining Data Curation Learning from Demonstration
Department of Mechanical Engineering University of California, Berkeley
Securing Self-supervised Data Curation for Foundation Models Robustness
Jun 8, 2026Sandeep Gupta, Roberto PasseroneTraining Data Curation Adversarial Example Detection
Centre for Secure Information Technologies (CSIT), Queen’s University Belfast, UK. · Department of Information Engineering and Computer Science, University of Trento, Italy.
Can Data Work be Reparative?
Jun 8, 2026Srravya Chandhiramowuli, Ding Wang, Alex TaylorAI Accountability Training Data Curation
University of Edinburgh, UK · Google Research, USA
AgriGov: A Structured Multilingual Dataset Curation for Indian Government Schemes for Farmers
Jun 6, 2026Mohsina Bilal, Gopakumar GTraining Data Curation Low-Resource MT
Computer Science and Engineering Department, National Institute of Technology Calicut, Kozhikode, 673601, Kerala, India.

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MedPMC: A Systematic Framework for Scaling High-Fidelity Medical Multimodal Data for Foundation Models

Hyunjae Kim, Dain Kim, Pan Xiao, Serina S. Applebaum+24

Yale University, New Haven, CT, USA · Korea University, Seoul, South Korea · Washington University in St. Louis, St. Louis, MO, USA+4

Medical Imaging Foundation Models Training Data Curation Multimodal Pretraining Medical VLMs Multimodal Foundation Models Medical Image Benchmarks

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Medicine is inherently multimodal, requiring clinicians to synthesize information across diverse data streams. Yet the development of multimodal foundation models is constrained by limited access to large-scale, high-quality clinical data. Although PubMed Central (PMC) offers a complementary source of expert-authored image-text data, existing PMC-derived resources remain limited in fidelity, reproducibility, and clinical validation. We introduce MedPMC, an automated, continuously updatable framework that transforms permissively licensed literature into high-fidelity infrastructure for medical multimodal models. Applied to 6.1 million PMC articles, MedPMC curated 11 million medical image-text pairs. Component evaluations showed strong performance for initial screening (F1 = 93.2), multi-panel figure detection (F1 = 96.5), figure separation (mAP = 89.8), caption separation and alignment (F1 = 81.4; ROUGE-L = 85.3), and medical figure classification (F1 = 96.5). Manual review by five annotators, three with medical training, found 95.3% of MedPMC images medically relevant, versus 19.7% in a prior PMC-derived dataset. Across 26 benchmarks spanning 11 specialties, a MedPMC-trained CLIP-style model improved average zero-shot AUC by 7.1 percentage points over the strongest architecture-matched biomedical CLIP baseline despite using fewer than half as many image-text pairs. As the vision encoder in a multimodal large language model, it improved medical visual question-answering by 1.9 and 16.9 percentage points across two benchmarks. In 10,524 Yale New Haven Health System dermatology photographs, it improved morphology-to-image retrieval Recall@5 by 11.7 percentage points. These findings show that high-fidelity literature curation strengthens medical multimodal foundation models across benchmark and clinical settings. We publicly release the framework, corpus, benchmarks, and pretrained models.

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Latest papers 104