---
title: "Can GPT-4 Perform Neural Architecture Search? — Zheng 2023"
description: "The authors ask whether GPT-4, used without any fine-tuning, can help with Neural Architecture… 85 references, cited by 23 papers here."
authors: ["Mingkai Zheng", "Xiu Su", "Shan You", "Fei Wang", "Chen Qian", "Chang Xu", "Samuel Albanie"]
year: 2023
venue: "arXiv:2304.10970"
source: "https://arxiv.org/abs/2304.10970"
doi: "10.48550/arxiv.2304.10970"
arxiv: "2304.10970"
licence: "CC BY 4.0 (arXiv)"
licence_url: "http://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/zheng-2023-gpt-4-perform-neural-architecture/"
---
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# Can GPT-4 Perform Neural Architecture Search?

**Authors:** Mingkai Zheng, Xiu Su, Shan You, [Fei Wang](https://md.research.elamur.ai/free/en/authors/fei-wang/), [Chen Qian](https://md.research.elamur.ai/free/en/authors/chen-qian/), [Chang Xu](https://md.research.elamur.ai/free/en/authors/chang-xu/) and [Samuel Albanie](https://md.research.elamur.ai/free/en/authors/samuel-albanie/) · **Year:** 2023 · **Venue:** arXiv:2304.10970 · **Licence:** [CC BY 4.0 (arXiv)](http://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://arxiv.org/abs/2304.10970) · [PDF](https://arxiv.org/pdf/2304.10970) · [DOI 10.48550/arxiv.2304.10970](https://doi.org/10.48550/arxiv.2304.10970) · [arXiv 2304.10970](https://arxiv.org/abs/2304.10970) · [OpenAlex](https://openalex.org/W4366999185)

**Citations:** 15 citations (OpenAlex, 2026-10-14) · [23](https://md.research.elamur.ai/free/en/papers/zheng-2023-gpt-4-perform-neural-architecture/cited-by/) in our library, of the 149,439 papers whose bibliographies we read

## Summary
The authors ask whether GPT-4, used without any fine-tuning, can help with Neural Architecture Search (NAS), a task that usually requires substantial expert knowledge. Their method, GENIUS, treats GPT-4 as a black-box optimiser. The search problem is described in plain text, and GPT-4 proposes an architecture configuration. That model is then trained or looked up and evaluated, and its accuracy is fed back through a fixed prompt asking for a better design with a justification. The loop repeats for a set number of rounds. The approach is tested on NAS-Bench-Macro and Channel-Bench-Macro, which contain precomputed CIFAR-10 results, against a random-sampling baseline. The best discovered architecture is also compared with leading NAS methods on a widely used benchmark. GENIUS outperforms random search and is competitive with established techniques, although the authors stress that state-of-the-art results were not their goal. The work matters because it suggests general-purpose LLMs can act as optimisers with minimal domain input. This raises research opportunities and also AI-safety concerns, such as eroding human know-how and enabling model self-improvement.

## Key points
- **Benchmarks:**
  - NAS-Bench-Macro has 8 searchable layers, each with 3 candidate blocks, giving 3^8 = 6561 architectures.
  - Channel-Bench-Macro has 7 layers, each with 4 width options, giving 4^7 = 16,384 architectures. It uses MobileNet and ResNet base models.
  - Both benchmarks provide CIFAR-10 results.
- **Random baseline:** the best of 10 random samples, averaged over 10,000 repetitions.
- **GENIUS runs:** 10 iterations per run, repeated 3 times, at GPT-4 temperatures 0 and 1.
- **Stated limitations:**
  - The opaque OpenAI API and nonzero variance even at temperature 0 hurt reproducibility.
  - Possible benchmark contamination of GPT-4's training data cannot be ruled out.
  - On NAS-Bench-201, later iterations sometimes perform worse than earlier ones, for unclear reasons.
- **Code:** available at github.com/mingkai-zheng/GENIUS.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [University of Cambridge](https://md.research.elamur.ai/free/en/universities/university-of-cambridge/) — University, Cambridge, United Kingdom · 287 works · 125,174 citations
- [The University of Sydney](https://md.research.elamur.ai/free/en/universities/the-university-of-sydney/) — University, Sydney, Australia · 52 works · 68,450 citations
- [Sensetime](https://md.research.elamur.ai/free/en/companies/sensetime/) — Company, Beijing, China · 14 works · 2,199 citations

📚 Shelves (3): [🖥️ Computer Science](https://md.research.elamur.ai/free/en/shelves/computer-science/) · 19,503 works · [🤖 Artificial Intelligence](https://md.research.elamur.ai/free/en/shelves/computer-science/artificial-intelligence/) · 18,805 works · [👁️ Computer Vision and Pattern Recognition](https://md.research.elamur.ai/free/en/shelves/computer-science/computer-vision-and-pattern-recognition/) · 3,621 works

🧭 Topics (1): [🧠 Language models & pretraining](https://md.research.elamur.ai/free/en/topics/language-models/) · 4,145 works

## Cite

Cite as: Mingkai Zheng, Xiu Su, Shan You, Fei Wang, Chen Qian, Chang Xu and Samuel Albanie. 2023. Can GPT-4 Perform Neural Architecture Search?. arXiv:2304.10970.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@misc{zheng2023can,
  title = {{Can GPT-4 Perform Neural Architecture Search?}},
  author = {Mingkai Zheng, Xiu Su, Shan You, Fei Wang, Chen Qian, Chang Xu and Samuel Albanie},
  year = {2023},
  doi = {10.48550/arxiv.2304.10970},
  eprint = {2304.10970},
  archivePrefix = {arXiv},
  url = {https://doi.org/10.48550/arxiv.2304.10970}
}
```

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