PatternGPT :A Pattern-Driven Framework for Large Language Model Text Generation
This work addresses the problem of unreliable text generation in critical real-world applications for users of large language models, but it appears incremental as it builds on existing ideas like federated learning and pattern extraction.
The paper tackles the challenges of hallucinations and lack of external knowledge in large language models for real-world tasks by proposing PatternGPT, a pattern-driven framework that generates diversified patterns, integrates external knowledge, and improves generation quality, providing an effective method for optimizing text generation in applications like intelligent dialogue and content creation.
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.