In the realm of machine learning, traditional model development and automated approaches like AutoML typically rely on layers of abstraction, such as tree-based or Cartesian genetic programming. Our study introduces "Guided Evolution" (GE), a novel framework that diverges from these methods by utilizing Large Language Models (LLMs) to directly modify code. GE leverages LLMs for a more intelligent, supervised evolutionary process, guiding mutations and crossovers. Our unique "Evolution of Thought" (EoT) technique further enhances GE by enabling LLMs to reflect on and learn from the outcomes of previous mutations. This results in a self-sustaining feedback loop that augments decision-making in model evolution. GE maintains genetic diversity, crucial for evolutionary algorithms, by leveraging LLMs' capability to generate diverse responses from expertly crafted prompts and modulate model temperature. This not only accelerates the evolution process but also injects expert like creativity and insight into the process. Our application of GE in evolving the ExquisiteNetV2 model demonstrates its efficacy: the LLM-driven GE autonomously produced variants with improved accuracy, increasing from 92.52% to 93.34%, without compromising model compactness. This underscores the potential of LLMs to accelerate the traditional model design pipeline, enabling models to autonomously evolve and enhance their own designs.

翻译：在机器学习领域，传统模型开发及AutoML等自动化方法通常依赖于树型或笛卡尔遗传编程等抽象层。本研究提出"引导演化"（GE）这一新颖框架，它通过利用大型语言模型（LLMs）直接修改代码，与传统方法形成鲜明对比。GE借助LLMs实现更智能的监督式演化过程，指导突变与交叉操作。我们独创的"演化思维链"（EoT）技术进一步强化了GE，使LLMs能够反思并学习先前突变的结果，形成自我维持的反馈循环以增强模型演化中的决策能力。通过利用LLMs从专业设计的提示中生成多样化响应及调节模型温度的能力，GE有效维持了遗传多样性——这一演化算法的关键要素。这不仅加速了演化进程，更将专家级的创造力与洞察力注入其中。我们在ExquisiteNetV2模型演化中的应用验证了其有效性：LLM驱动的GE自主生成了准确率从92.52%提升至93.34%的改进变体，且未牺牲模型紧凑性。这凸显了LLMs加速传统模型设计管线的潜力，使模型能够自主进化并优化自身设计。