PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation

TL;DR

PatternGPT is a pattern-driven framework that enhances large language model text generation by using structured patterns, external knowledge, and federated learning to improve diversity, quality, and applicability in real-world tasks.

Contribution

This paper introduces PatternGPT, a novel framework that leverages pattern extraction, external knowledge, and federated learning to improve LLM text generation quality and diversity.

Findings

Generates diversified structured patterns for better knowledge integration.

Enhances generation quality and diversity through pattern optimization.

Protects data privacy while improving model outputs.

Abstract

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.