mlsanityData to 14 Oct 2026

Data-centric Artificial Intelligence: A Survey

arXiv 2303.10158 ↗First posted 17 Mar 2023cs.LG cs.AI cs.DBcited by 203

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Abstract (arXiv)

Artificial Intelligence (AI) is making a profound impact in almost every domain. A vital enabler of its great success is the availability of abundant and high-quality data for building machine learning models. Recently, the role of data in AI has been significantly magnified, giving rise to the emerging concept of data-centric AI. The attention of researchers and practitioners has gradually shifted from advancing model design to enhancing the quality and quantity of the data. In this survey, we discuss the necessity of data-centric AI, followed by a holistic view of three general data-centric goals (training data development, inference data development, and data maintenance) and the representative methods. We also organize the existing literature from automation and collaboration perspectives, discuss the challenges, and tabulate the benchmarks for various tasks. We believe this is the first comprehensive survey that provides a global view of a spectrum of tasks across various stages of the data lifecycle. We hope it can help the readers efficiently grasp a broad picture of this field, and equip them with the techniques and further research ideas to systematically engineer data for building AI systems. A companion list of data-centric AI resources will be regularly updated on https://github.com/daochenzha/data-centric-AI

Key facts. Cited by 203 papers in the map (data through 14 Oct 2026). Builds on 144 of its 309 references that are in the map. First posted on arXiv 17 Mar 2023.

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144 of 309 references.

References · 144

  1. 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,399
  2. 2020Language Models are Few-Shot Learnerscited by 32,419
  3. 2020Denoising Diffusion Probabilistic Modelscited by 21,076
  4. 2021High-Resolution Image Synthesis with Latent Diffusion Modelscited by 18,863
  5. 2022Training language models to follow instructions with human feedbackcited by 15,971
  6. 2017Towards Deep Learning Models Resistant to Adversarial Attackscited by 6,415
  7. 2017mixup: Beyond Empirical Risk Minimizationcited by 5,183
  8. 2017Deep reinforcement learning from human preferencescited by 3,912
  9. 2013Playing Atari with Deep Reinforcement Learningcited by 3,274
  10. 2019Benchmarking Neural Network Robustness to Common Corruptions and Perturbationscited by 2,877
  11. 2015Character-level Convolutional Networks for Text Classificationcited by 2,330
  12. 2021Pre-train, Prompt, and Predict: A Systematic Survey of Prompting Methods in Natural Language Processingcited by 2,234
38 more
  1. 2022Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language modelscited by 1,998
  2. 2019A Survey on Bias and Fairness in Machine Learningcited by 1,917
  3. 2016Adversarial examples in the physical worldcited by 1,769
  4. 2018Graph Neural Networks: A Review of Methods and Applicationscited by 1,576
  5. 2020WILDS: A Benchmark of in-the-Wild Distribution Shiftscited by 1,375
  6. 2015DeepFool: a simple and accurate method to fool deep neural networkscited by 1,318
  7. 2020Making Pre-trained Language Models Better Few-shot Learnerscited by 1,256
  8. 2021Cascaded Diffusion Models for High Fidelity Image Generationcited by 1,163
  9. 2021Variational Diffusion Modelscited by 1,153
  10. 2017Counterfactual Explanations without Opening the Black Box: Automated Decisions and the GDPRcited by 1,089
  11. 2015Aligning Books and Movies: Towards Story-like Visual Explanations by Watching Movies and Reading Bookscited by 1,043
  12. 2019How Can We Know What Language Models Know?cited by 959
  13. 2020Exploiting Cloze Questions for Few Shot Text Classification and Natural Language Inferencecited by 938
  14. 2016Practical Black-Box Attacks against Machine Learningcited by 903
  15. 2018Self-Attention Generative Adversarial Networkscited by 809
  16. 2018AutoAugment: Learning Augmentation Policies from Datacited by 730
  17. 2019Universal Adversarial Triggers for Attacking and Analyzing NLPcited by 722
  18. 2021BARTScore: Evaluating Generated Text as Text Generationcited by 722
  19. 2017Evasion Attacks against Machine Learning at Test Timecited by 689
  20. 2020It's Not Just Size That Matters: Small Language Models Are Also Few-Shot Learnerscited by 669
  21. 2017ZOO: Zeroth Order Optimization based Black-box Attacks to Deep Neural Networks without Training Substitute Modelscited by 666
  22. 2019Data Shapley: Equitable Valuation of Data for Machine Learningcited by 629
  23. 2020A Survey of Deep Active Learningcited by 554
  24. 2018Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networkscited by 519
  25. 2021Retiring Adult: New Datasets for Fair Machine Learningcited by 449
  26. 2021Towards Out-Of-Distribution Generalization: A Surveycited by 436
  27. 2021A Survey of Data Augmentation Approaches for NLPcited by 409
  28. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  29. 2018Detecting and Correcting for Label Shift with Black Box Predictorscited by 366
  30. 2016Time Series Classification from Scratch with Deep Neural Networks: A Strong Baselinecited by 360
  31. 2020Rethinking Pre-training and Self-trainingcited by 352
  32. 2021Domain Adaptation for Medical Image Analysis: A Surveycited by 352
  33. 2020A Brief Review of Domain Adaptationcited by 288
  34. 2020Algorithmic Recourse: from Counterfactual Explanations to Interventionscited by 250
  35. 2016Data Programming: Creating Large Training Sets, Quicklycited by 246
  36. 2019FACE: Feasible and Actionable Counterfactual Explanationscited by 233
  37. 2020Time Series Data Augmentation for Deep Learning: A Surveycited by 226
  38. 2018Multiaccuracy: Black-Box Post-Processing for Fairness in Classificationcited by 222

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Citing papers per year: 2023: 44; 2024: 50; 2025: 55; 2026: 54.

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  1. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 411
  2. 2023Skeleton-of-Thought: Prompting LLMs for Efficient Parallel Generationcited by 105
  3. 2023Make-An-Audio 2: Temporal-Enhanced Text-to-Audio Generationcited by 90
  4. 2024A Survey of Multimodal Large Language Model from A Data-centric Perspectivecited by 75
  5. 2024Large Language Models for Cyber Security: A Systematic Literature Reviewcited by 68
  6. 2023FinGPT: Democratizing Internet-scale Data for Financial Large Language Modelscited by 67
  7. 2024Clustering and Ranking: Diversity-preserved Instruction Selection through Expert-aligned Quality Estimationcited by 53
  8. 2023When Do Annotator Demographics Matter? Measuring the Influence of Annotator Demographics with the POPQUORN Datasetcited by 52
  9. 2023Curated LLM: Synergy of LLMs and Data Curation for tabular augmentation in low-data regimescited by 52
  10. 2025Empowering Edge Intelligence: A Comprehensive Survey on On-Device AI Modelscited by 46
  11. 2023Differentially Private Low-Rank Adaptation of Large Language Model Using Federated Learningcited by 41
  12. 2024A Comprehensive Survey on Data Augmentationcited by 40
38 more
  1. 2023FuseCap: Leveraging Large Language Models for Enriched Fused Image Captionscited by 33
  2. 2023KnowGPT: Knowledge Graph based Prompting for Large Language Modelscited by 31
  3. 2023Pre-RMSNorm and Pre-CRMSNorm Transformers: Equivalent and Efficient Pre-LN Transformerscited by 30
  4. 2023OpenGSL: A Comprehensive Benchmark for Graph Structure Learningcited by 30
  5. 2024Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challengescited by 30
  6. 2024Unleashing the Power of Data Tsunami: A Comprehensive Survey on Data Assessment and Selection for Instruction Tuning of Language Modelscited by 29
  7. 2023Compress, Then Prompt: Improving Accuracy-Efficiency Trade-off of LLM Inference with Transferable Promptcited by 28
  8. 2024Data-Centric Foundation Models in Computational Healthcare: A Surveycited by 27
  9. 2023The Efficiency Spectrum of Large Language Models: An Algorithmic Surveycited by 26
  10. 2024Scaling Data Diversity for Fine-Tuning Language Models in Human Alignmentcited by 26
  11. 2023Visual Analytics For Machine Learning: A Data Perspective Surveycited by 25
  12. 2023MediTab: Scaling Medical Tabular Data Predictors via Data Consolidation, Enrichment, and Refinementcited by 24
  13. 2023TabLib: A Dataset of 627M Tables with Contextcited by 23
  14. 2024Towards Urban General Intelligence: A Review and Outlook of Urban Foundation Modelscited by 23
  15. 2023Data-centric Graph Learning: A Surveycited by 22
  16. 2024A Survey on Evaluation of Out-of-Distribution Generalizationcited by 21
  17. 2023AlpaGasus: Training A Better Alpaca with Fewer Datacited by 19
  18. 2023Data Management For Training Large Language Models: A Surveycited by 17
  19. 2023README: Bridging Medical Jargon and Lay Understanding for Patient Education through Data-Centric NLPcited by 17
  20. 2025Agent Data Protocol: Unifying Datasets for Diverse, Effective Fine-tuning of LLM Agentscited by 17
  21. 2024Towards Data-Centric Face Anti-Spoofing: Improving Cross-domain Generalization via Physics-based Data Synthesiscited by 16
  22. 2023Dynamic Datasets and Market Environments for Financial Reinforcement Learningcited by 15
  23. 2023Towards Data-centric Graph Machine Learning: Review and Outlookcited by 15
  24. 2024The Synergy between Data and Multi-Modal Large Language Models: A Survey from Co-Development Perspectivecited by 15
  25. 2023Comparing Foundation Models using Data Kernelscited by 14
  26. 2023Annotation Sensitivity: Training Data Collection Methods Affect Model Performancecited by 14
  27. 2024EXMOS: Explanatory Model Steering Through Multifaceted Explanations and Data Configurationscited by 14
  28. 2024The METRIC-framework for assessing data quality for trustworthy AI in medicine: a systematic reviewcited by 13
  29. 2023Chasing Fairness in Graphs: A GNN Architecture Perspectivecited by 12
  30. 2024LongWanjuan: Towards Systematic Measurement for Long Text Qualitycited by 11
  31. 2024An Experimental Study on the Rashomon Effect of Balancing Methods in Imbalanced Classificationcited by 11
  32. 2025Text2SQL-Flow: A Robust SQL-Aware Data Augmentation Framework for Text-to-SQLcited by 11
  33. 2026AI4AI-Bench: Benchmarking LLM Agents in Algorithmic Design for Recursive Self-Improvementcited by 11
  34. 2024Outlier Gradient Analysis: Efficiently Identifying Detrimental Training Samples for Deep Learning Modelscited by 10
  35. 2024Federated Data-Efficient Instruction Tuning for Large Language Modelscited by 10
  36. 2026Can We Predict Before Executing Machine Learning Agents?cited by 10
  37. 2023Winner-Take-All Column Row Sampling for Memory Efficient Adaptation of Language Modelcited by 9
  38. 2023A Deep Learning Approach for Overall Survival Prediction in Lung Cancer with Missing Valuescited by 9

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