LLMatic：基于大语言模型与质量多样性优化的神经架构搜索
LLMatic: Neural Architecture Search via Large Language Models and Quality Diversity Optimization
AI总结：
该研究提出结合大语言模型代码生成能力与质量多样性优化的神经架构搜索算法LLMatic，通过过程化方法生成多样高性能网络，在仅评估2000个候选的情况下于两个基准上取得有竞争力的结果。
摘要：
大语言模型（LLM）已成为能够完成广泛任务的强大工具，其能力覆盖众多领域，其中产生显著影响的一个领域是代码生成领域。本文提出利用LLM的代码生成能力，为定义神经网络的代码引入有意义的变异。同时，已知质量多样性（QD）算法能够发现多样且鲁棒的解。通过将LLM的代码生成能力与QD解的多样性和鲁棒性相结合，本文提出了\texttt{LLMatic}——一种神经架构搜索（NAS）算法。尽管LLM难以通过提示直接执行NAS，但\texttt{LLMatic}采用过程化方法，利用QD生成提示并优化网络架构，从而创建出多样且高性能的网络。我们在CIFAR-10和NAS-bench-201基准上测试了\texttt{LLMatic}，结果表明，即使不具备基准领域的先验知识，也未接触过该基准之前的任何最优模型，该算法仅需评估2000个候选架构，就能生成具有竞争力的网络。开源代码可在https://github.com/umair-nasir14/LLMatic 获取。
英文摘要：
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. The open-sourced code is available in \url{https://github.com/umair-nasir14/LLMatic}.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
