What Gets Unmasked First? Trajectory Analysis of Diffusion Models for Graph-to-Text Generation
arXiv:2605.31564v1 Announce Type: cross Abstract: We present the first systematic study of masked diffusion language models (MDLMs) for graph-to-text generation. We analyze MDLM generation trajectories -- the order in which tokens are unmasked during iterative decoding -- and find that, unlike autoregressive LLMs which generate text linearly, MDLMs naturally prioritize entities first, followed by relational and function words, with structural tokens resolved last. We further identify a previously undocumented failure mode of supervised fine-tuning: SFT disrupts this strategy by prematurely anc
This research provides new insights into the working mechanisms of diffusion models for text generation, a rapidly evolving area within AI research.
Understanding the internal 'thought process' of these models, like prioritizing entities, can lead to more robust, controllable, and efficient graph-to-text generation.
The explicit identification of a failure mode in supervised fine-tuning (SFT) for masked diffusion language models highlights the need for new training paradigms that preserve or enhance their natural generation strategies.
- · AI researchers
- · Generative AI developers
- · NLP applications
- · Inefficient SFT methods
- · Over-standardized text generation approaches
Improved understanding of diffusion model mechanics in text generation.
Development of refined training techniques for graph-to-text generative models that leverage their intrinsic unmasking order.
More nuanced and context-aware AI agents capable of higher fidelity information synthesis from structured data.
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Read at arXiv cs.AI