Syntax-Guided Diffusion Language Models with User-Integrated Personalization

• URL: http://arxiv.org/abs/2510.01028v1
• Date: Wed, 01 Oct 2025 15:33:12 GMT
• Title: Syntax-Guided Diffusion Language Models with User-Integrated Personalization
• Authors: Ruqian Zhang, Yijiao Zhang, Juan Shen, Zhongyi Zhu, Annie Qu,
• Abstract summary: Large language models have made revolutionary progress in generating human-like text, yet their outputs often tend to be generic.<n>Recent advances in diffusion models have opened new opportunities for improving language generation beyond the limitations of autoregressive paradigms.<n>We propose a syntax-guided diffusion language model that integrates structural supervision and personalized conditioning to enhance text quality, diversity, and controllability.
• Score: 1.202131801903952
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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