DLM4G Graph-Conditioned Diffusion Language Framework
Figure 1 : DLM4G framework: (A) Graph-Sequence alignment set { 𝒜 \mathcal{A} }, obtains the aligned tokens; (B) The model is trained with a graph-aware noising schedule (C) Trained DLM4G samples output sequence conditioned on graph.
논문 컨텍스트
Paper title: Factual and Edit-Sensitive Graph-to-Sequence Generation via Graph-Aware Adaptive Noising Abstract: 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. The proposed strategy uses per-token denoising error as a signal to adaptively modulate noise on entity and relation tokens, improving preservation of graph structure and enabling localized updates under graph edits. Evaluated on three datasets, DLM4G consistently outperforms competitive G2S diffusion baselines trained on identical splits across both surface-form and embedding-based metrics. DLM4G further exceeds fine-tuned autoregressive baselines up to 12x larger (e.g., T5-Large) and is competitive with zero-shot LLM transfer baselines up to 127x larger. Relative to the strongest fine-tuned PLM baseline, DLM4G improves factual grounding (FGT@0.5) by +5.16% and edit sensitivity (ESR) by +7.9%; compared to the best diffusion baseline, it yields gains of +3.75% in FGT@0.5 and +23.6% in ESR. We additionally demonstrate applicability beyond textual graphs through experiments on molecule captioning, indicating the method's generality for scientific G2S generation. Passages referencing this figure: t},\mathbf{c})\Big)}_{\text{Denoising matching }(L_{t-1})}\\ +\underbrace{D_{\mathrm{KL}}\!\Big(q(\mathbf{z}_{T}\mid\mathbf{z}_{0})\ \|\ p(\mathbf{z}_{T})\Big)}_{\text{Prior matching }(L_{T})}\Big].\end{aligned} (1) While tractable, direct optimization of the full VLB is often unstable (Li et al. , 2022 ) ; in later sections we show the modified objective with graph-aware, component-wise noising. Figure 1 : DLM4G framework: (A) Graph-Sequence alignment set { 𝒜 \mathcal{A} }, obtains the aligned tokens; (B) The model is trained with a graph-aware noising schedule (C) Trained DLM4G samples output sequence conditioned on graph. 3 The DLM4G Methodology 3.1 Problem Setting: Graph-to-Sequence Generation Let 𝒢 = ( 𝒱 , ℰ , 𝐗 ) \mathcal{G}=(\mathcal{V},\mathcal{E},\mathbf{X}) denote an input graph,
