aiqbench analysis
This research introduces DA-DLM, a novel architecture for Diffusion Language Models that explicitly models inter-token dependencies using a position-oriented Directed Acyclic Graph to mitigate coherence issues inherent in parallel generation. By adapting DAG-based methods from non-autoregressive translation, the model achieves performance comparable to autoregressive baselines while retaining speed advantages with fewer denoising steps. The work represents a significant methodological advance in addressing the conditional independence limitations of current diffusion-based text generation systems.
DA-DLM: Explicitly Modeling Token Dependencies in Diffusion Language Models
arXiv:2609.15070v1 Announce Type: new Abstract: Diffusion Language Models (DLMs) generate text by iteratively denoising a masked sequence, independently predicting multiple tokens at each step. This conditional independence discards inter-token dependencies and degrades coherence-an issue that parallels the multi-modality problem in Non-Autoregressive Translation (NAT). Drawing on the Directed Acyclic Transformer (DAT), which tackles this problem in NAT via a Directed Acyclic Graph (DAG), we propose DA-DLM, a model that adapts DAG-based dependency modeling to DLMs' iterative setting through a position-oriented DAG design. The position-oriented DAG binds node groups to fixed output positions so that tokens fixed in earlier steps anchor neighboring predictions via learned transitions, and evolves with denoising to focus on remaining uncertainty as anchors accumulate. On language modeling, open-ended generation, and summarization, DA-DLM consistently outperforms Block Diffusion, especially under fewer denoising steps, and matches autoregressive models while preserving the parallel generation advantage. Our code is publicly available at https://github.com/jipy0222/DA-DLM.
Abstract:Diffusion Language Models (DLMs) generate text by iteratively denoising a masked sequence, independently predicting multiple tokens at each step. This conditional independence discards inter-token dependencies and degrades coherence-an issue that parallels the multi-modality problem in Non-Autoregressive Translation (NAT). Drawing on the Directed Acyclic Transformer (DAT), which tackles this problem in NAT via a Directed Acyclic Graph (DAG), we propose DA-DLM, a model that adapts DAG-based dependency modeling to DLMs' iterative setting through a position-oriented DAG design. The position-oriented DAG binds node groups to fixed output positions so that tokens fixed in earlier steps anchor neighboring predictions via learned transitions, and evolves with denoising to focus on remaining uncertainty as anchors accumulate. On language modeling, open-ended generation, and summarization, DA-DLM consistently outperforms Block Diffusion, especially under fewer denoising steps, and matches autoregressive models while preserving the parallel generation advantage. Our code is publicly available at this https URL.
Computation and Language (cs.CL)
arXiv:2609.15070 [cs.CL]
(or arXiv:2609.15070v1 [cs.CL] for this version)
https://doi.org/10.48550/arXiv.2609.15070 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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