@inproceedings{koh-etal-2025-conditional,
title = "Conditional [{MASK}] Discrete Diffusion Language Model",
author = "Koh, Hyukhun and
Jhang, Minha and
Kim, Dohyung and
Lee, Sangmook and
Jung, Kyomin",
editor = "Christodoulopoulos, Christos and
Chakraborty, Tanmoy and
Rose, Carolyn and
Peng, Violet",
booktitle = "Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2025",
address = "Suzhou, China",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.emnlp-main.450/",
pages = "8910--8934",
ISBN = "979-8-89176-332-6",
abstract = "Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges, we propose Diffusion-EAGS, a novel framework that integrates conditional masked language models into diffusion language models through the theoretical lens of a conditional Markov Random Field. In doing so, we propose entropy-adaptive Gibbs sampling and entropy-based noise scheduling to counterbalance each model{'}s shortcomings. Experimental results show that Diffusion-EAGS outperforms baselines and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation."
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<abstract>Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges, we propose Diffusion-EAGS, a novel framework that integrates conditional masked language models into diffusion language models through the theoretical lens of a conditional Markov Random Field. In doing so, we propose entropy-adaptive Gibbs sampling and entropy-based noise scheduling to counterbalance each model’s shortcomings. Experimental results show that Diffusion-EAGS outperforms baselines and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation.</abstract>
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%0 Conference Proceedings
%T Conditional [MASK] Discrete Diffusion Language Model
%A Koh, Hyukhun
%A Jhang, Minha
%A Kim, Dohyung
%A Lee, Sangmook
%A Jung, Kyomin
%Y Christodoulopoulos, Christos
%Y Chakraborty, Tanmoy
%Y Rose, Carolyn
%Y Peng, Violet
%S Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing
%D 2025
%8 November
%I Association for Computational Linguistics
%C Suzhou, China
%@ 979-8-89176-332-6
%F koh-etal-2025-conditional
%X Although auto-regressive models excel in natural language processing, they often struggle to generate diverse text and provide limited controllability. Non-auto-regressive methods could be an alternative but often produce degenerate outputs and exhibit shortcomings in conditional generation. To address these challenges, we propose Diffusion-EAGS, a novel framework that integrates conditional masked language models into diffusion language models through the theoretical lens of a conditional Markov Random Field. In doing so, we propose entropy-adaptive Gibbs sampling and entropy-based noise scheduling to counterbalance each model’s shortcomings. Experimental results show that Diffusion-EAGS outperforms baselines and achieves the best quality-diversity tradeoff, demonstrating its effectiveness in non-autoregressive text generation.
%U https://aclanthology.org/2025.emnlp-main.450/
%P 8910-8934
Markdown (Informal)
[Conditional [MASK] Discrete Diffusion Language Model](https://aclanthology.org/2025.emnlp-main.450/) (Koh et al., EMNLP 2025)
ACL
- Hyukhun Koh, Minha Jhang, Dohyung Kim, Sangmook Lee, and Kyomin Jung. 2025. Conditional [MASK] Discrete Diffusion Language Model. In Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing, pages 8910–8934, Suzhou, China. Association for Computational Linguistics.