带有用户集成个性化的语法引导扩散语言模型
Syntax-Guided Diffusion Language Models with User-Integrated Personalization
AI总结：
本研究提出语法引导扩散语言模型，结合结构监督与个性化条件，通过级联及非级联架构、共享表示机制，在多任务实验中展现出文本生成的流畅度、多样性与风格保真度优势。
摘要：
大语言模型在生成类人文本方面取得了革命性进展，但其输出往往趋于通用化，结构多样性不足，限制了个性化表达。扩散模型的最新进展为突破自回归范式的局限、改进语言生成提供了新机遇。本研究提出一种语法引导扩散语言模型，它集成了结构监督与个性化条件，以提升文本质量、多样性和可控性。我们引入了一种级联框架，在条件文本生成前生成语法引导，并进一步将其推广到新颖的非级联架构，以实现结构与内容间更好的对齐。通过在生成过程中融入语法信息，所提模型能更好地捕捉风格语句构建的词汇与结构特征。为实现细粒度个性化，我们开发了一种共享表示机制，可促进跨用户的信息整合，支持忠实的风格生成与可泛化的零样本推理。在多个任务上开展的大量实验表明，我们的方法在流畅度、多样性和风格保真度方面具有优越性；进一步的定性分析凸显了其学习个性化模式的可解释性与灵活性。
英文摘要：
Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic, exhibiting insufficient structural diversity, which limits personalized expression. Recent advances in diffusion models have opened new opportunities for improving language generation beyond the limitations of autoregressive paradigms. In this work, we propose a syntax-guided diffusion language model that integrates structural supervision and personalized conditioning to enhance text quality, diversity, and controllability. We introduce a cascaded framework that generates syntactic guidance before conditional text generation, and further generalize it to a novel noncascaded architecture for better alignment between structure and content. By incorporating syntactic information in the generating process, the proposed model better captures the lexical and structural characteristics of stylistic sentence construction. To enable fine-grained personalization, we develop a shared representation mechanism that facilitates information integration across users, supporting both faithful stylistic generation and generalizable zero-shot inference. Extensive experiments on multiple tasks demonstrate the superiority of our approach in fluency, diversity, and stylistic fidelity. Further qualitative analyses highlight its interpretability and flexibility in learning personalized patterns.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
