Large language models(LLMs) have shown excellent text generation capabilities, but there is still much space for improvement in accuracy, sometimes with grammatical errors, semantic inaccuracies, and contextual incoherence, which seriously affect the reliability of the models. These problems may originate from the difficulties and limitations encountered in the pattern extraction stage of large language models. How to utilize the generative power of large language models to generate as many possible patterns that help solve problems and find the optimal patterns from them, so as to use patterns to guide large language models to generate good content, has become a current research hotspot. In this paper, we propose a pattern extraction and selection framework, PatternGPT, which generates rich patterns through the extraction ability of large language models and draws on the idea of federation learning, where multiple agents collaborate with each other to generate diverse patterns. High-quality patterns are selected by defining criteria and optimization algorithms to personalize the guidance of the model generation process. PatternGPT has the advantages of generating diverse and useful patterns, extending relevant knowledge, facilitating efficient pattern use and transfer, and optimizing the quality of generated results and user experience, which provides an effective method for optimizing the text generation capability of large language models and is expected to drive further development in the field of intelligent dialogue and content generation. It is expected to promote further development in the field of intelligent dialogue and content generation.

翻译：大型语言模型（LLMs）已展现出卓越的文本生成能力，但在精度方面仍有较大提升空间，偶尔会出现语法错误、语义不准确和上下文不连贯等问题，严重影响了模型的可靠性。这些问题可能源于大型语言模型在模式提取阶段遇到的困难与局限性。如何利用大型语言模型的生成能力，尽可能多地生成有助于解决任务的模式，并从中筛选出最优模式，从而以模式引导大型语言模型生成优质内容，已成为当前的研究热点。本文提出了一种模式提取与选择框架——PatternGPT，该框架通过大型语言模型的提取能力生成丰富模式，并借鉴联邦学习思想，使多个智能体相互协作以生成多样化的模式。通过定义标准与优化算法筛选高质量模式，从而个性化指导模型生成过程。PatternGPT具有生成多样且有用模式、扩展相关知识、促进模式高效利用与迁移、优化生成结果质量与用户体验等优势，为优化大型语言模型的文本生成能力提供了有效方法，有望推动智能对话与内容生成领域的进一步发展。