这篇论文揭示了扩散模型在图到文本任务中的独特解码机制，做结构化文本生成或知识图谱相关工作的开发者值得关注，尤其是SFT反而有害的发现可能改变你的微调策略。
本文首次系统研究了掩码扩散语言模型（MDLM）在图到文本生成中的解码轨迹，发现MDLM会优先解码实体，然后是关系词和功能词，最后才是结构词，这与自回归模型的线性生成方式不同。研究还发现监督微调（SFT）会破坏这一策略，过早固定结构词导致输出长度固定，引发信息遗漏或幻觉。为此，作者提出了一种无需训练的推理时修改方法——lambda缩放结构解码，将BLEU-4提升9.4分。最后，他们提出了Graph-LLaDA，将图Transformer编码器集成到LLaDA的解码过程中，显式利用关系图结构。跨数据集评估表明，基于LLM和MDLM的方法泛化能力显著优于传统基线。
What Gets Unmasked First? Trajectory Analysis of Diffusion Models for Graph-to-Text Generation
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 anchoring structural sentence-ending tokens early in the decoding trajectory, effectively fixing the output length which can lead to omitted or hallucinated information. To address this, we propose lambda-scaled structural decoding, a training-free inference-time modification that downweights structural token confidence and recovers +9.4 BLEU-4. Finally, we introduce Graph-LLaDA, which integrates a Graph Transformer encoder into LLaDA's decoding process to explicitly incorporate relational graph structure. Cross-dataset evaluation on LAGRANGE reveals that previous baselines overfit to dataset-specific patterns, while LLM- and MDLM-based approaches generalize significantly better.