mlsanityData to 7 Oct 2026

ModelGPT: Unleashing LLM's Capabilities for Tailored Model Generation

arXiv 2402.12408 ↗First posted 18 Feb 2024cs.LG cs.AI cs.CLcited by 11

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Abstract (arXiv)

The rapid advancement of Large Language Models (LLMs) has revolutionized various sectors by automating routine tasks, marking a step toward the realization of Artificial General Intelligence (AGI). However, they still struggle to accommodate the diverse and specific needs of users and simplify the utilization of AI models for the average user. In response, we propose ModelGPT, a novel framework designed to determine and generate AI models specifically tailored to the data or task descriptions provided by the user, leveraging the capabilities of LLMs. Given user requirements, ModelGPT is able to provide tailored models at most 270x faster than the previous paradigms (e.g. all-parameter or LoRA finetuning). Comprehensive experiments on NLP, CV, and Tabular datasets attest to the effectiveness of our framework in making AI models more accessible and user-friendly. Our code is available at https://github.com/IshiKura-a/ModelGPT.

Key facts. Cited by 11 papers in the map (data through 7 Oct 2026). Builds on 26 of its 43 references that are in the map. First posted on arXiv 18 Feb 2024.

Builds on

26 of 43 references.

References · 26

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14 more
  1. 2023A Comprehensive Overview of Large Language Modelscited by 639
  2. 2018Deep Visual Domain Adaptation: A Surveycited by 634
  3. 2023Textbooks Are All You Needcited by 561
  4. 2019Generalizing from a Few Examples: A Survey on Few-Shot Learningcited by 462
  5. 2023Harnessing the Power of LLMs in Practice: A Survey on ChatGPT and Beyondcited by 409
  6. 2023A Survey on Large Language Model (LLM) Security and Privacy: The Good, the Bad, and the Uglycited by 380
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Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 5; 2025: 4; 2026: 2.

11 papers.

  1. 2025AutoReproduce: Automatic AI Experiment Reproduction with Paper Lineagecited by 29
  2. 2024Large Language Models for Constructing and Optimizing Machine Learning Workflows: A Surveycited by 19
  3. 2024Semantic Alignment for Multimodal Large Language Modelscited by 12
  4. 2024Text-to-Model: Text-Conditioned Neural Network Diffusion for Train-Once-for-All Personalizationcited by 10
  5. 2026Mnemis: Dual-Route Retrieval on Hierarchical Graphs for Long-Term LLM Memorycited by 7
  6. 2024Semantic Codebook Learning for Dynamic Recommendation Modelscited by 6
  7. 2024AuG-KD: Anchor-Based Mixup Generation for Out-of-Domain Knowledge Distillationcited by 5
  8. 2025Instruction-Guided Autoregressive Neural Network Parameter Generationcited by 3
  9. 2025Tailoring Diagnostic Modeling to Individual Learners: Personalized Distractor Generation via MCTS-Guided Reasoning Reconstructioncited by 2
  10. 2025Tackling Device Data Distribution Real-time Shift via Prototype-based Parameter Editingcited by 0
  11. 2026Will It Zero-Shot?: Predicting Zero-Shot Classification Performance For Arbitrary Queriescited by 0