@inproceedings{zhou-etal-2026-diffusion,
title = "Diffusion with Truncated Blocks: Fast and High-Quality Text Generation using Truncated Block Generation",
author = "Zhou, Yuyan and
Chen, Weiyu and
Kwok, James",
editor = "Liakata, Maria and
Moreira, Viviane P. and
Zhang, Jiajun and
Jurgens, David",
booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {ACL} 2026",
month = jul,
year = "2026",
address = "San Diego, California, United States",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.findings-acl.212/",
pages = "4335--4348",
ISBN = "979-8-89176-395-1",
abstract = "Diffusion-based Large Language Models (dLLMs) are emerging as a powerful alternative to traditional autoregressive models. These models learn to generate text by iteratively denoising masked sequences. In this work, we identify a critical problem in dLLMs: the model{'}s attention is wastefully expended on uninformative mask tokens, diluting its focus on meaningful context. We term this phenomenon ``attention dilution''. We further show that this artifact is amplified by token-level noising, whereas models employing sequence-level noise exhibit a reduced effect. To resolve this problem, we introduce Truncated Block Generation, a novel sampling algorithm that not only mitigates attention dilution but also enables faster inference and flexible-length sequence generation. Extensive experiments validate our analysis and demonstrate the marked effectiveness of our proposed method in enhancing both the performance and efficiency of dLLMs."
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<abstract>Diffusion-based Large Language Models (dLLMs) are emerging as a powerful alternative to traditional autoregressive models. These models learn to generate text by iteratively denoising masked sequences. In this work, we identify a critical problem in dLLMs: the model’s attention is wastefully expended on uninformative mask tokens, diluting its focus on meaningful context. We term this phenomenon “attention dilution”. We further show that this artifact is amplified by token-level noising, whereas models employing sequence-level noise exhibit a reduced effect. To resolve this problem, we introduce Truncated Block Generation, a novel sampling algorithm that not only mitigates attention dilution but also enables faster inference and flexible-length sequence generation. Extensive experiments validate our analysis and demonstrate the marked effectiveness of our proposed method in enhancing both the performance and efficiency of dLLMs.</abstract>
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%0 Conference Proceedings
%T Diffusion with Truncated Blocks: Fast and High-Quality Text Generation using Truncated Block Generation
%A Zhou, Yuyan
%A Chen, Weiyu
%A Kwok, James
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Findings of the Association for Computational Linguistics: ACL 2026
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-395-1
%F zhou-etal-2026-diffusion
%X Diffusion-based Large Language Models (dLLMs) are emerging as a powerful alternative to traditional autoregressive models. These models learn to generate text by iteratively denoising masked sequences. In this work, we identify a critical problem in dLLMs: the model’s attention is wastefully expended on uninformative mask tokens, diluting its focus on meaningful context. We term this phenomenon “attention dilution”. We further show that this artifact is amplified by token-level noising, whereas models employing sequence-level noise exhibit a reduced effect. To resolve this problem, we introduce Truncated Block Generation, a novel sampling algorithm that not only mitigates attention dilution but also enables faster inference and flexible-length sequence generation. Extensive experiments validate our analysis and demonstrate the marked effectiveness of our proposed method in enhancing both the performance and efficiency of dLLMs.
%U https://aclanthology.org/2026.findings-acl.212/
%P 4335-4348
Markdown (Informal)
[Diffusion with Truncated Blocks: Fast and High-Quality Text Generation using Truncated Block Generation](https://aclanthology.org/2026.findings-acl.212/) (Zhou et al., Findings 2026)
ACL