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. In this context, we view LLMs as mutation and crossover tools. 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 LLMatic, a Neural Architecture Search (NAS) algorithm. While LLMs struggle to conduct NAS directly through prompts, LLMatic uses a procedural approach, leveraging QD for prompts and network architecture to create diverse and highly performant networks. We test LLMatic on the CIFAR-10 image classification benchmark, demonstrating that it can produce competitive networks with just $2,000$ searches, even without prior knowledge of the benchmark domain or exposure to any previous top-performing models for the benchmark.

翻译：摘要：大语言模型（LLMs）已发展为能够完成广泛任务的强大工具，其能力覆盖众多领域，尤其在代码生成方面产生了显著影响。在此背景下，我们将LLMs视为变异与交叉工具。与此同时，质量多样性（QD）算法以发现多样且鲁棒的解决方案而著称。通过融合LLMs的代码生成能力与QD解决方案的多样性和鲁棒性，我们提出LLMatic——一种神经架构搜索（NAS）算法。尽管LLMs难以直接通过提示符进行NAS，LLMatic采用程序化方法，利用QD优化提示符与网络架构，从而生成多样且高性能的网络结构。我们在CIFAR-10图像分类基准上测试LLMatic，结果表明即使对该基准领域无先验知识，也未曾接触过该基准上先前性能最优模型，仅需2,000次搜索即可生成具有竞争力的网络结构。