mlsanityData to 7 Oct 2026

AutoML-GPT: Automatic Machine Learning with GPT

arXiv 2305.02499 ↗First posted 4 May 2023cs.CL cs.AI cs.CVcited by 80

Not on the map. Why?

Cited by 22 in LLM agents and tool use, but placed by its own abstract, which didn’t fit.

Abstract (arXiv)

AI tasks encompass a wide range of domains and fields. While numerous AI models have been designed for specific tasks and applications, they often require considerable human efforts in finding the right model architecture, optimization algorithm, and hyperparameters. Recent advances in large language models (LLMs) like ChatGPT show remarkable capabilities in various aspects of reasoning, comprehension, and interaction. Consequently, we propose developing task-oriented prompts and automatically utilizing LLMs to automate the training pipeline. To implement this concept, we present the AutoML-GPT, which employs GPT as the bridge to diverse AI models and dynamically trains models with optimized hyperparameters. AutoML-GPT dynamically takes user requests from the model and data cards and composes the corresponding prompt paragraph. Ultimately, with this prompt paragraph, AutoML-GPT will automatically conduct the experiments from data processing to model architecture, hyperparameter tuning, and predicted training log. By leveraging ours's robust language capabilities and the available AI models, AutoML-GPT can tackle numerous intricate AI tasks across various tasks and datasets. This approach achieves remarkable results in computer vision, natural language processing, and other challenging areas. Extensive experiments and ablation studies demonstrate that our method can be general, effective, and beneficial for many AI tasks.

Key facts. Cited by 80 papers in the map (data through 7 Oct 2026). Builds on 29 of its 33 references that are in the map. First posted on arXiv 4 May 2023.

Builds on

29 of 33 references.

References · 29

  1. 2021Learning Transferable Visual Models From Natural Language Supervisioncited by 33,456
  2. 2020Language Models are Few-Shot Learnerscited by 32,199
  3. 2023GPT-4 Technical Reportcited by 18,132
  4. 2014Microsoft COCO: Common Objects in Contextcited by 17,498
  5. 2023LLaMA: Open and Efficient Foundation Language Modelscited by 13,958
  6. 2022Chain-of-Thought Prompting Elicits Reasoning in Large Language Modelscited by 12,302
  7. 2021Swin Transformer: Hierarchical Vision Transformer using Shifted Windowscited by 12,082
  8. 2022Self-Consistency Improves Chain of Thought Reasoning in Language Modelscited by 5,601
  9. 2022PaLM: Scaling Language Modeling with Pathwayscited by 5,304
  10. 2020Dense Passage Retrieval for Open-Domain Question Answeringcited by 3,779
  11. 2022Emergent Abilities of Large Language Modelscited by 2,483
  12. 2016Matching Networks for One Shot Learningcited by 2,246
17 more
  1. 2018Datasheets for Datasetscited by 1,371
  2. 2018Unified Perceptual Parsing for Scene Understandingcited by 1,265
  3. 2020Leveraging Passage Retrieval with Generative Models for Open Domain Question Answeringcited by 1,203
  4. 2021Scaling Language Models: Methods, Analysis & Insights from Training Gophercited by 1,199
  5. 2023HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Facecited by 1,173
  6. 2018Model Cards for Model Reportingcited by 1,068
  7. 2021An Explanation of In-context Learning as Implicit Bayesian Inferencecited by 915
  8. 2022STaR: Bootstrapping Reasoning With Reasoningcited by 895
  9. 2023In-Context Retrieval-Augmented Language Modelscited by 694
  10. 2023Visual ChatGPT: Talking, Drawing and Editing with Visual Foundation Modelscited by 659
  11. 2022Using DeepSpeed and Megatron to Train Megatron-Turing NLG 530B, A Large-Scale Generative Language Modelcited by 557
  12. 2023OpenAGI: When LLM Meets Domain Expertscited by 196
  13. 2023ART: Automatic multi-step reasoning and tool-use for large language modelscited by 193
  14. 2023Can GPT-4 Perform Neural Architecture Search?cited by 74
  15. 2021Knowing More About Questions Can Help: Improving Calibration in Question Answeringcited by 47
  16. 2023Interactive-Chain-Prompting: Ambiguity Resolution for Crosslingual Conditional Generation with Interactioncited by 16
  17. 2022Passage-Mask: A Learnable Regularization Strategy for Retriever-Reader Modelscited by 6

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Cited by year (2026 to 7 Oct)

Citing papers per year: 2023: 14; 2024: 29; 2025: 21; 2026: 16.

80 papers.

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  1. 2023MLAgentBench: Evaluating Language Agents on Machine Learning Experimentationcited by 339
  2. 2023Multimodal Foundation Models: From Specialists to General-Purpose Assistantscited by 247
  3. 2024DS-Agent: Automated Data Science by Empowering Large Language Models with Case-Based Reasoningcited by 119
  4. 2024DiscoveryBench: Towards Data-Driven Discovery with Large Language Modelscited by 97
  5. 2024Evolutionary Computation in the Era of Large Language Model: Survey and Roadmapcited by 89
  6. 2024AutoML-Agent: A Multi-Agent LLM Framework for Full-Pipeline AutoMLcited by 84
  7. 2023Using Large Language Models for Hyperparameter Optimizationcited by 71
  8. 2024MLR-Copilot: Autonomous Machine Learning Research based on Large Language Models Agentscited by 64
  9. 2024Large Language Model Agent for Hyper-Parameter Optimizationcited by 62
  10. 2025ML-Master: Towards AI-for-AI via Integration of Exploration and Reasoningcited by 57
  11. 2023Algorithm Evolution Using Large Language Modelcited by 52
  12. 2023AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Riskscited by 34
38 more
  1. 2024Large Language Models for UAVs: Current State and Pathways to the Futurecited by 33
  2. 2023Large Language Model for Multi-objective Evolutionary Optimizationcited by 29
  3. 2025AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineagecited by 29
  4. 2024Large Language Model-Based Evolutionary Optimizer: Reasoning with elitismcited by 27
  5. 2025ML-Agent: Reinforcing LLM Agents for Autonomous Machine Learning Engineeringcited by 26
  6. 2023Large Language Models Streamline Automated Machine Learning for Clinical Studiescited by 24
  7. 2025Dolphin: Moving Towards Closed-loop Auto-research through Thinking, Practice, and Feedbackcited by 23
  8. 2024Understanding the Importance of Evolutionary Search in Automated Heuristic Design with Large Language Modelscited by 22
  9. 2025Large Language Model-based Data Science Agent: A Surveycited by 22
  10. 2024When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challengescited by 20
  11. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  12. 2024Self-Evolved Reward Learning for LLMscited by 16
  13. 2025AgentTTS: Large Language Model Agent for Test-time Compute-optimal Scaling Strategy in Complex Taskscited by 16
  14. 2024AutoElicit: Using Large Language Models for Expert Prior Elicitation in Predictive Modellingcited by 15
  15. 2024AutoMMLab: Automatically Generating Deployable Models from Language Instructions for Computer Vision Taskscited by 14
  16. 2024Position: A Call to Action for a Human-Centered AutoML Paradigmcited by 14
  17. 2024Design Principle Transfer in Neural Architecture Search via Large Language Modelscited by 14
  18. 2025AstroVisBench: A Code Benchmark for Scientific Computing and Visualization in Astronomycited by 13
  19. 2025Reinforcement Learning for Machine Learning Engineering Agentscited by 13
  20. 2023Sentiment Analysis through LLM Negotiationscited by 11
  21. 2023TrainerAgent: Customizable and Efficient Model Training through LLM-Powered Multi-Agent Systemcited by 8
  22. 2024Large Language Models Synergize with Automated Machine Learningcited by 8
  23. 2024Automating Traffic Model Enhancement with AI Research Agentcited by 8
  24. 2025From Hypothesis to Publication: A Comprehensive Survey of AI-Driven Research Support Systemscited by 8
  25. 2025DatawiseAgent: A Notebook-Centric LLM Agent Framework for Adaptive and Robust Data Science Automationcited by 8
  26. 2023Heterogeneous Graph Neural Architecture Search with GPT-4cited by 7
  27. 2025SPIO: Ensemble and Selective Strategies via LLM-Based Multi-Agent Planning in Automated Data Sciencecited by 7
  28. 2026WirelessAgent++: Automated Agentic Workflow Design and Benchmarking for Wireless Networkscited by 7
  29. 2024Bag of Tricks for Multimodal AutoML with Image, Text, and Tabular Datacited by 6
  30. 2026LongDA: Benchmarking LLM Agents for Long-Document Data Analysiscited by 6
  31. 2023GPT vs Human for Scientific Reviews: A Dual Source Review on Applications of ChatGPT in Sciencecited by 5
  32. 2026Agentic Bayesian Optimization through Surrogate-Augmented Autoresearchcited by 5
  33. 2024Language Rectified Flow: Advancing Diffusion Language Generation with Probabilistic Flowscited by 4
  34. 2025Representing LLMs in Prompt Semantic Task Spacecited by 3
  35. 2026Delta-Based Neural Architecture Search: LLM Fine-Tuning via Code Diffscited by 3
  36. 2024Large Language Models for Tuning Evolution Strategiescited by 2
  37. 2024CliMB: An AI-enabled Partner for Clinical Predictive Modelingcited by 2
  38. 2024Adaptive Augmentation Policy Optimization with LLM Feedbackcited by 2

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