Data Selection

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Data selection studies which examples, sources, labels, or interactions a model should learn from, aiming to improve performance while reducing training or annotation costs. Common approaches include coreset and diversity methods, uncertainty or active learning, gradient and influence scoring, distribution matching, and adaptive sampling policies. These methods are used in settings such as image classification, LLM instruction and preference tuning, multimodal training, and clinical labeling. Selected subsets can match or outperform full-data training, but usefulness depends on the model, target task, and budget; a key challenge is balancing short-term gains with coverage of rare cases, robustness, and long-term learning.

Latest papers

84 papers10 papers in the last four weeks, level with the four weeks before. 0.1% of all new papers.

Team DACTYL at PAN 2026: Bayesian Data Mixing and Empirical X-risk Minimization for AI-text Detection
Jul 19, 2026Shantanu ThoratAI-Generated Text Detection Data Selection
College Station, Texas, US
Beyond Random Sampling: Distribution-Aware Alignment for Semi-Supervised Medical Image Segmentation
Jul 5, 2026Weihao Yan, Yeqiang Qian, Yi Dong +1Data Augmentation Data Selection
School of Automation and Intelligent Sensing, Shanghai Jiao Tong University, Shanghai, China · Department of Ultrasound, Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, China
Co-Adaptive Multi-Task LoRA: Transfer-Aware, Label-Free Control of Domain Participation
Jul 3, 2026Wei Zhang, Lin Tang, Ming Zhao +1Multi-Task Learning Data Selection
1Sichuan University, Chengdu, China · University of Electronic Science and Technology of China, Chengdu, China
Structural Assessment for Understanding and Guiding Dataset Distillation in Discrete Token Space
Jun 19, 2026Yue Cao, Jianyang Gu, Vyacheslav Kungurtsev +4Data Selection Dataset Distillation
The University of British Columbia · The Ohio State University · Czech Technical University in Prague +1
Which Pairs to Compare for LLM Post-Training?
Jun 17, 2026Jiangze Han, Vineet Goyal, Will MaPairwise Preference Learning Pairwise Comparison
Columbia University
Active Learning with Low-Rank Structure for Data Selection
Jun 14, 2026Vincent Cohen-Addad, Sasidhar Kunapuli, Vahab Mirrokni +3Active Learning Data Selection
Google Research. · University of California, Berkeley. · Institute of Science and Technology Austria (ISTA). +2
HARP: Efficient Data Selection for Finetuning Large Language Models
Jun 5, 2026Ning Wang, Zhengxin Zhang, Maosen Tang +3LLM Fine-Tuning Active Learning
Cornell University · The Hong Kong University of Science and Technology
GRASP: Geometry-aware Residual Alignment for Scalable Pretraining Data Attribution
Jun 5, 2026Yue Min, Ruining Chen, Yujun LiData Selection Training Data Selection
Wizard Quant · University of Science and Technology of China
Causal Modeling of Selection in Evolution
Jun 4, 2026Haoyue Dai, Zeyu Tang, Peter Spirtes +1Structural Causal Models Causal Discovery
Carnegie Mellon University · Stanford University · Mohamed bin Zayed University of Artificial Intelligence
STRIDE: Training Data Attribution via Sparse Recovery from Subset Perturbations
Jun 3, 2026Rishit Dagli, Abir Harrasse, Luke Zhang +4Sparse Recovery Data Selection
Jinesis AI Lab, University of Toronto & Vector Institute · Max Planck Institute for Intelligent Systems, Tübingen, Germany · Thoughtworks +3
Can Generalist Agents Automate Data Curation?
Jun 2, 2026Feiyang Kang, Hanze Li, Adam Nguyen +5AI Coding Agents Training Data Curation
1Virginia Tech · University of Illinois Urbana-Champaign · University of Wisconsin-Madison +1
PaddleOCR-VL-1.6: Expanding the Frontier of Document Parsing with Under-Optimized Region Refinement and Progressive Post-Training
Jun 2, 2026Zelun Zhang, Hongen Liu, Suyin Liang +12Data Selection Training Data Selection
PaddlePaddle Team, Baidu Inc.
Two to Tango: Coupled Task-Reference Selection for Safe LLM Fine-tuning
Jun 1, 2026Xinrui Chen, Jianhao Zhang, Ou Wu +1LLM Fine-Tuning Data Selection
Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences
Trust Functions: Near-Lossless Weak-to-Strong Generalization by Learning When to Trust the Weak Teacher
May 31, 2026Arda Uzunoglu, Alvin Zhang, Daniel KhashabiData Selection Training Data Selection
Department of Computer Science, Johns Hopkins University, Maryland, United States.
Sample-Efficient Post-Training for LEGO Spatial-Physics Reasoning
May 29, 2026Yuhuan Yuan, Zhouliang Yu, Minghao Liu +2Physical Reasoning Visual Spatial Reasoning
HKUST(GZ) · CUHK · ZODA
Adaptive data selection improves wearable prediction under low baseline performance
May 29, 2026Ali KargarandehkordiData Selection Training Data Selection
The Long-Term Effects of Data Selection in LLM Fine-Tuning
May 28, 2026Yuxin Yang, Aoxiong Zeng, Xiangquan YangContinual Learning for LLMs LLM Fine-Tuning
Shanghai University · East China Normal University
MIRA: Mid-training Rubric Anchoring for Source-Aware Data Selection
May 28, 2026Haowen Wang, Yaxin Du, Jian Yang +9Data Selection Efficient Language Model Training
1Beihang University · 2IQuest Research · 3Shanghai Jiao Tong University +2
Memory-Distilled Selection for Noise-Robust Anomaly Detection
May 26, 2026Sirojbek Safarov, Jaewoo Park, Yoon Gyo Jung +4Industrial Anomaly Detection Data Selection
AIVEX Inc. · Northeastern University · Mitsubishi Electric Research Laboratories (MERL).
Complement Submodular Information Measures for Balanced and Robust Data Selection
May 23, 2026Rishabh IyerSubmodular Optimization Data Selection
Department of Computer Science The University of Texas at Dallas Richardson, TX 75080
SEED: Targeted Data Selection by Weighted Independent Set
May 15, 2026Yuan Zhang, Lifeng Guo, Junwen Pan +5Training Data Curation Instruction-Tuning Data Selection
School of Computer Science, Peking University · Beijing University of Posts and Telecommunications · Tianjin University +2
Beyond What to Select: A Plug-and-play Oscillatory Data-Volume Scheduling for Efficient Model Training
May 14, 2026Suorong Yang, Hanqi Zhu, Hai Gan +4Deep Learning Optimization Data Selection
1National University of Singapore · 2Nanjing University · 3Hokkaido University +1
Towards a holistic understanding of Selection Bias for Causal Effect Identification
May 13, 2026Yiwen Qiu, Filip Kovačević, Shimeng Huang +2Causal Effect Estimation Data Selection
Institute of Science and Technology Austria (ISTA) · Carnegie Mellon University
From Instance Selection to Fixed-Pool Data Recipe Search for Supervised Fine-Tuning
May 13, 2026Haodong Wu, Jiahao Zhang, Lijie Hu +1Supervised Fine-Tuning Training Data Curation
The Hong Kong University of Science and Technology (Guangzhou) · Mohamed bin Zayed University of Artificial Intelligence
How Faithful Is Trajectory-Based Data Attribution? Error Sources, Remedies, and Practical Guidelines
May 12, 2026Junwei Deng, Pingbang Hu, Suliang Jin +4Gradient-Based Attribution Data Selection
University of Illinois Urbana-Champaign · University of Michigan · Princeton University +1
CAST: Collapse-Aware multi-Scale Topology Fusion for Multimodal Coreset Selection
May 12, 2026Boran Zhao, Hetian Liu, Zhenxian Hu +3Data Selection Distribution Matching
School of Software Engineering Xi’an Jiaotong University · the National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, National Engineering Research Center for Visual Information and Applications, and Institute of Artificial Intelligence and Robotics Xi’an Jiaotong University
Is Data Shapley Not Better than Random in Data Selection? Ask NASH
May 11, 2026Xiao Tian, Jue Fan, Rachael Hwee Ling Sim +3Data Selection Training Data Selection
Department of Computer Science, National University of Singapore, Singapore · Agency for Science, Technology and Research (A*STAR), Singapore
Let the Target Select for Itself: Data Selection via Target-Aligned Paths
May 10, 2026Huitao Yang, Hengzhi He, Tung Sum Thomas Kwok +1Data Selection Training Data Selection
University of California, Los Angeles
CREST: Curvature-Regulated Event-Centric Sampling for Efficient Long-Video Understanding
May 9, 2026Mehrajul Abadin Miraj, Abdul Mohaimen Al Radi, Shariful Islam Rayhan +4Data Selection Long-Video Understanding
Dept. of CSE, University of Dhaka · Dept. of CSE, University of Central Florida
Diversity-Aware Active Learning Builds Materials Datasets for Current and Future Objectives
May 6, 2026Rafael Espinosa Castañeda, Ashley Dale, Hongchen Wang +6Diversity-Aware Sampling Data Selection
Department of Materials Science and Engineering, University of Toronto, Canada. · Vector Institute for Artificial Intelligence, Toronto, Canada · Department of Computer Science, Princeton University +2

Jul 19, 2026J/K move · Enter open · S save

Team DACTYL at PAN 2026: Bayesian Data Mixing and Empirical X-risk Minimization for AI-text Detection

Shantanu Thorat

College Station, Texas, US

AI-Generated Text Detection Data Selection OOD Detection Robustness of Machine-Generated Text Detection Empirical Risk Minimization

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Existing research shows that AI-generated text detection classifiers achieve strong in-distribution (ID) performance but do not maintain the same performance on out-of-distribution (OOD) texts, suggesting overfitting to dataset-specific features. However, combining different training datasets doesn't always improve performance and, in some cases, can even encourage shortcut learning. To address this issue, we fine-tune BERT-tiny models with Bayesian classification heads to select texts across three different datasets to use as a consolidated training set. We trained three different classifiers: fine-tuned DeBERTa-V3-large and ModernBERT-large classifiers via empirical X-risk minimization, and an MCGrad model that calibrates the predictions from the ModernBERT-large classifier. The DeBERTa-V3-large-large classifier achieves a mean score of 0.882 on the PAN 2026 test set across five metrics: AUROC,

F_1

, C@1, Brier score, and

F_{0.5u}

. ModernBERT-large achieves a score of 0.96 while MCGrad achieves the best score of the three with a mean score of 0.974, ranking second on the leaderboard. Our results highlight that careful dataset curation can lead to strong OOD performance. We release our ModernBERT-large and DeBERTa-V3-large models at https://huggingface.co/collections/ShantanuT01/panclef-2026 .