DA-DLM: Explicitly Modeling Token Dependencies in Diffusion Language Models

2026-09-14

AI Summary

DA-DLM通过引入位置导向的有向无环图来显式建模扩散语言模型中的词元依赖关系，从而提高生成文本的一致性。

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.

Problem

• Diffusion Language Models
• inter-token dependencies
• coherence

Innovation

• Directed Acyclic Graph (DAG)
• Token Dependencies
• Position-Oriented Design
• Diffusion Language Models (DLMs)