AI RESEARCH
Factual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising
arXiv CS.CL
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ArXi:2604.24104v1 Announce Type: new Fine-tuned autoregressive models for graph-to-sequence generation (G2S) often struggle with factual grounding and edit sensitivity. To tackle these issues, we propose a non-autoregressive diffusion framework that generates text by iterative refinement conditioned on an input graph, named as Diffusion Language Model for Graphs (DLM4G). By aligning graph components (entities/relations) with their corresponding sequence tokens, DLM4G employs an adaptive noising strategy.