  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.
