Large language models(LLMS)have shown excellent text generation capabilities, capable of generating fluent human-like responses for many downstream tasks. However, applying large language models to real-world critical tasks remains challenging due to their susceptibility to hallucinations and inability to directly use external knowledge. To cope with the above challenges, this paper proposes PatternGPT, a pattern-driven text generation framework for Large Language Models. Firstly, the framework utilizes the extraction capability of Large Language Models to generate rich and diversified structured and formalized patterns, which facilitates the introduction of external knowledge to do the computation, and then draws on the idea of federated learning to use multiple agents to achieve the sharing in order to obtain more diversified patterns, and finally uses judgment criteria and optimization algorithm to search for high-quality patterns to guide the generation of models. Finally, external knowledge such as judgment criteria and optimization algorithms are used to search for high-quality patterns, and the searched patterns are used to guide model generation. This framework has the advantages of generating diversified patterns, protecting data privacy, combining external knowledge, and improving the quality of generation, which provides an effective method to optimize the text generation capability of large language models, and make it better applied to the field of intelligent dialogue and content generation.

翻译：大型语言模型（LLMs）展现出卓越的文本生成能力，能够针对许多下游任务生成流畅且类似人类的响应。然而，由于大型语言模型易产生幻觉、且无法直接利用外部知识，将其应用于现实世界的关键任务仍具挑战。为应对上述挑战，本文提出PatternGPT——一种面向大型语言模型的模式驱动文本生成框架。首先，该框架利用大型语言模型的抽取能力生成丰富多样的结构化与形式化模式，这便于引入外部知识进行计算；随后借鉴联邦学习思想，通过多智能体实现模式共享以获得更多元化的模式；最后使用判断准则与优化算法搜索高质量模式，并利用搜索到的模式指导模型生成。此框架具有生成多样化模式、保护数据隐私、结合外部知识及提升生成质量等优势，为优化大型语言模型的文本生成能力提供了有效方法，使其能更好地应用于智能对话与内容生成领域。