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        <title>Unlocking the Potential of Diffusion Language Models through Template Infilling</title>
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    <abstract>Diffusion Language Models (DLMs) have emerged as a promising alternative to Autoregressive Language Models, yet their inference strategies largely rely on prefix-based prompting inherited from the autoregressive paradigm. In this paper, we propose Template Infilling (TI), a conditioning methodology tailored for DLMs. Unlike conventional prefix prompting, TI distributes structural anchors across the target response, establishing a global template before infilling masked segments. This enables structured conditioning that leverages the bidirectional generation process of DLMs. We evaluate TI on diverse benchmarks, including mathematical reasoning, code generation, and trip planning, achieving consistent improvements of 9.40%p over baseline prompting strategies. Furthermore, TI naturally supports multi-token generation settings, providing practical speed advantages while maintaining generation quality and robustness. Overall, our results highlight a DLM-specific conditioning paradigm for structured generation, suggesting a promising direction for inference methods tailored to diffusion-based language models.</abstract>
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%0 Conference Proceedings
%T Unlocking the Potential of Diffusion Language Models through Template Infilling
%A Lee, Junhoo
%A Kim, Seungyeon
%A Kwak, Nojun
%Y Liakata, Maria
%Y Moreira, Viviane P.
%Y Zhang, Jiajun
%Y Jurgens, David
%S Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2026
%8 July
%I Association for Computational Linguistics
%C San Diego, California, United States
%@ 979-8-89176-390-6
%F lee-etal-2026-unlocking
%X Diffusion Language Models (DLMs) have emerged as a promising alternative to Autoregressive Language Models, yet their inference strategies largely rely on prefix-based prompting inherited from the autoregressive paradigm. In this paper, we propose Template Infilling (TI), a conditioning methodology tailored for DLMs. Unlike conventional prefix prompting, TI distributes structural anchors across the target response, establishing a global template before infilling masked segments. This enables structured conditioning that leverages the bidirectional generation process of DLMs. We evaluate TI on diverse benchmarks, including mathematical reasoning, code generation, and trip planning, achieving consistent improvements of 9.40%p over baseline prompting strategies. Furthermore, TI naturally supports multi-token generation settings, providing practical speed advantages while maintaining generation quality and robustness. Overall, our results highlight a DLM-specific conditioning paradigm for structured generation, suggesting a promising direction for inference methods tailored to diffusion-based language models.
%U https://aclanthology.org/2026.acl-long.284/
%P 6273-6287

Markdown (Informal)

[Unlocking the Potential of Diffusion Language Models through Template Infilling](https://aclanthology.org/2026.acl-long.284/) (Lee et al., ACL 2026)

• Unlocking the Potential of Diffusion Language Models through Template Infilling (Lee et al., ACL 2026)

ACL

• Junhoo Lee, Seungyeon Kim, and Nojun Kwak. 2026. Unlocking the Potential of Diffusion Language Models through Template Infilling. In Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 6273–6287, San Diego, California, United States. Association for Computational Linguistics.