Large Language Models (LLMs) have emerged as powerful tools capable of accomplishing a broad spectrum of tasks. Their abilities span numerous areas, and one area where they have made a significant impact is in the domain of code generation. Here, we propose using the coding abilities of LLMs to introduce meaningful variations to code defining neural networks. Meanwhile, Quality-Diversity (QD) algorithms are known to discover diverse and robust solutions. By merging the code-generating abilities of LLMs with the diversity and robustness of QD solutions, we introduce \texttt{LLMatic}, a Neural Architecture Search (NAS) algorithm. While LLMs struggle to conduct NAS directly through prompts, \texttt{LLMatic} uses a procedural approach, leveraging QD for prompts and network architecture to create diverse and high-performing networks. We test \texttt{LLMatic} on the CIFAR-10 and NAS-bench-201 benchmarks, demonstrating that it can produce competitive networks while evaluating just $2,000$ candidates, even without prior knowledge of the benchmark domain or exposure to any previous top-performing models for the benchmark.
Clone this repository:
git clone https://github.com/umair-nasir14/LLMatic.git
Install all dependencies:
cd LLMatic
conda env create -f environment.yaml
conda activate llmatic
Run:
python llmatic.py
All configs are present in conf/config.py.
@article{nasir2023llmatic,
title={Llmatic: Neural architecture search via large language models and quality-diversity optimization},
author={Nasir, Muhammad U and Earle, Sam and Togelius, Julian and James, Steven and Cleghorn, Christopher},
journal={arXiv preprint arXiv:2306.01102},
year={2023}
}