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.

本文提出了一种基于模式提取和选择的框架，PatternGPT，通过生成大型语言模型的抽取能力和联邦学习的思想来生成丰富的模式，并通过定义标准和优化算法来选择高品质的模式，并优化生成结果和用户体验的质量，从而提高大型语言模型的文本生成能力，有望促进智能对话和内容生成领域的进一步发展。