arXiv:2601.21647v2 Announce Type: replace-cross
Abstract: Discrete Diffusion Language Models (DLMs) offer a promising non-autoregressive alternative for text generation, yet effective mechanisms for...
By Eden Avrahami, Eliya Nachmani
arXiv:2508. 10875v3 Announce Type: replace-cross Abstract: Diffusion Language Models (DLMs) are rapidly emerging as a powerful and promising alternative to the dominant autoregressive (AR) paradigm.
By Tianyi Li, Mingda Chen, Bowei Guo, Zhiqiang Shen
arXiv:2609.07160v1 Announce Type: cross
Abstract: Diffusion Large Language Models (dLLMs) generate text via bidirectional iterative denoising, naturally supporting user-specified constraints anchored...
By Zheng Nie, Zherui Li, Jiaming Zhang, Kun Wang, Zhenhong Zhou, Yufei Guo
The paper introduces a syntax-guided diffusion language model that incorporates structural supervision and personalized conditioning to improve text quality, diversity, and controllability. It presents a cascaded framework generating syntactic guidance before text generation, and a novel noncascaded architecture for better structure-content alignment. A shared representation mechanism enables fine‑grained personalization across users, achieving faithful stylistic generation and zero‑shot inference, with experiments showing superior fluency, diversity, and stylistic fidelity.
By Ruqian Zhang, Yijiao Zhang, Juan Shen, Zhongyi Zhu, Annie Qu
SenseShift is an encoder-based framework that enables fine‑grained, sentence‑level sentiment control in text generation. It uses bidirectional attention, quantized sentiment signals, and iterative mask infilling to generate local sentences conditioned on target sentiment intensity. Experiments on story and review generation show that SenseShift delivers stronger sentiment controllability while preserving text quality and robustness to out‑of‑domain inputs compared to larger decoder‑based baselines.
By Shahed Masoudian, Markus Frohmann, Emmanouil Karystinaios, Navid Rekabsaz, Markus Schedl
arXiv:2606. 19475v1 Announce Type: new Abstract: Large Language Models (LLMs) have revolutionized language modeling through autoregressive generation, enabling strong performance across a wide range of tasks.
By Thomas Bertolani, Davide Bucciarelli, Leonardo Zini, Marcella Cornia, Lorenzo Baraldi