标题： 带有用户集成个性化的语法引导扩散语言模型 显示英文标题

标题： Syntax-Guided Diffusion Language Models with User-Integrated Personalization

摘要： 大型语言模型在生成类似人类的文本方面取得了革命性的进展，但它们的输出往往趋于通用，表现出结构多样性不足，这限制了个性化表达。 扩散模型的最新进展为超越自回归范式的局限性改进语言生成提供了新的机遇。 在本工作中，我们提出了一种语法引导的扩散语言模型，该模型结合了结构监督和个性化条件，以提高文本质量、多样性和可控性。 我们引入了一个级联框架，在条件文本生成之前生成语法指导，并进一步将其推广到一种新颖的非级联架构，以实现结构与内容之间的更好对齐。 通过在生成过程中结合语法信息，所提出的模型更好地捕捉了风格化句子构造的词汇和结构特征。 为了实现细粒度的个性化，我们开发了一种共享表示机制，该机制促进了跨用户的信息整合，支持忠实的风格生成和可推广的零样本推理。 在多个任务上的大量实验表明，我们的方法在流畅性、多样性和风格保真度方面具有优势。 进一步的定性分析突显了其在学习个性化模式中的可解释性和灵活性。 显示英文摘要

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.