Abstract
In machine learning, Neural Architecture Search (NAS) requires domain knowledge of model design and a large amount of trial-and-error to achieve promising performance. Meanwhile, evolutionary algorithms have traditionally relied on fixed rules and pre-defined building blocks. The Large Language Model (LLM)-Guided Evolution (GE) framework transformed this approach by incorporating LLMs to directly modify model source code for image classification algorithms on CIFAR data and intelligently guide mutations and crossovers. A key element of LLM-GE is the "Evolution of Thought" (EoT) technique, which establishes feedback loops, allowing LLMs to refine their decisions iteratively based on how previous operations performed. In this study, we perform NAS for object detection by improving LLM-GE to modify the architecture of You Only Look Once (YOLO) models to enhance performance on the KITTI dataset. Our approach intelligently adjusts the design and settings of YOLO to find the optimal algorithms against objective such as detection accuracy and speed. We show that LLM-GE produced variants with significant performance improvements, such as an increase in Mean Average Precision from 92.5% to 94.5%. This result highlights the flexibility and effectiveness of LLM-GE on real-world challenges, offering a novel paradigm for automated machine learning that combines LLM-driven reasoning with evolutionary strategies.
Abstract (translated)
在机器学习领域，神经架构搜索（NAS）需要模型设计的专业知识和大量的试错才能取得令人满意的表现。与此同时，传统的进化算法依赖于固定的规则和预先定义的构建模块。大型语言模型（LLM）引导演化（GE）框架通过将LLMs融入其中，直接修改图像分类算法在CIFAR数据集上的模型源代码，并智能地指导变异与交叉过程，从而改变了这一方法。LLM-GE的关键元素是“思想进化”(EoT)技术，它建立了反馈循环机制，使LLMs能够根据之前的操作表现来迭代改进其决策。 在这项研究中，我们通过优化LLM-GE框架，在对象检测任务上进行神经架构搜索（NAS），以改进You Only Look Once (YOLO)模型的架构，并在KITTI数据集上提高性能。我们的方法智能地调整和设置YOLO的设计参数，以便找到针对准确性和速度等目标的最佳算法。我们展示了LLM-GE生成了具有显著性能提升的新变体，例如平均精度均值从92.5%提升至94.5%。 这一结果强调了LLM-GE在解决实际问题时的灵活性和有效性，为自动机器学习提供了一种新的范式，结合了由大型语言模型驱动的推理与进化策略。
URL
https://arxiv.org/abs/2504.02280
