Large language models(LLMS) have shown excellent text generation capabilities,capable of generating fluent 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 address the above challenges,this paper proposes PatternGPT, a pattern-driven text generation framework for large language models. First,the framework utilizes the extraction capabilities of large language models to generate rich and diverse patterns and later draws on the idea of federated learning. Using multiple agents to achieve sharing to obtain more diverse patterns. Finally, it searches for high-quality patterns using judgment criteria and optimization algorithms and uses the searched patterns to guide the model for 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：大语言模型(LLMs)展现出卓越的文本生成能力，可为众多下游任务生成流畅的回答。然而，由于大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有挑战性。为解决上述难题，本文提出PatternGPT —— 一种面向大语言模型的模式驱动文本生成框架。该框架首先利用大语言模型的提取能力生成丰富多样的模式，随后借鉴联邦学习的思想，通过多个智能体实现模式共享以获取更多样化的模式。最后，利用判断准则和优化算法搜索高质量模式，并利用搜索到的模式指导模型进行文本生成。该框架具备生成多样化模式、保护数据隐私、融合外部知识以及提升生成质量等优势，为大语言模型文本生成能力的优化提供了有效途径，使其能更好地应用于智能对话与内容生成领域。