当大型语言模型遇上进化算法：潜在的增强与挑战
When Large Language Models Meet Evolutionary Algorithms: Potential Enhancements and Challenges
AI总结：
本文从微观概念对应和宏观跨学科研究两个层面，系统分析了大型语言模型与进化算法的相似性、结合机会及关键挑战，为增强人工智能智能体能力提供方向。
摘要：
预训练大型语言模型（LLMs）展现出生成自然文本的强大能力。进化算法（EAs）能够为复杂的现实问题发现多样化的解决方案。受文本生成与进化所共有的集体性和方向性启发，本文首先在微观层面阐释了LLMs与EAs之间的概念对应关系，其中包括多个一对一的关健特征：token表示与个体表示、位置编码与适应度塑造、位置嵌入与选择、Transformers模块与繁殖，以及模型训练与参数自适应。这些对应关系凸显了LLMs和EAs在技术改进方面的潜在机会。随后，我们从宏观视角分析现有的跨学科研究，以揭示关键挑战，特别关注进化微调和LLM增强的EAs。这些分析不仅为理解LLMs背后的进化机制提供了见解，也为增强人工智能智能体的能力提供了潜在方向。
英文摘要：
Pre-trained large language models (LLMs) exhibit powerful capabilities for generating natural text. Evolutionary algorithms (EAs) can discover diverse solutions to complex real-world problems. Motivated by the common collective and directionality of text generation and evolution, this paper first illustrates the conceptual parallels between LLMs and EAs at a micro level, which includes multiple one-to-one key characteristics: token representation and individual representation, position encoding and fitness shaping, position embedding and selection, Transformers block and reproduction, and model training and parameter adaptation. These parallels highlight potential opportunities for technical advancements in both LLMs and EAs. Subsequently, we analyze existing interdisciplinary research from a macro perspective to uncover critical challenges, with a particular focus on evolutionary fine-tuning and LLM-enhanced EAs. These analyses not only provide insights into the evolutionary mechanisms behind LLMs but also offer potential directions for enhancing the capabilities of artificial agents.
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。
