{"slug": "structurally-speaking-motif-oriented-graph-captioning-through-bidirectional-text", "title": "Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation", "summary": "A new arXiv paper (2609.10923v1) introduces \"Structurally Speaking,\" a lightweight structured prompting protocol that guides large language models to translate between explicit graph connectivity and motif-level abstraction for graph captioning. The authors report that direct prompting of GPT-5.1 often yields graph-recoverable captions but produces verbose output with inconsistent motif interpretations, while structured prompting on a synthetic motif-based dataset generated shorter, more motif-consistent captions with comparable graph recovery and no model fine-tuning.", "body_md": "arXiv:2609.10923v1 Announce Type: new \nAbstract: Graph captions should help readers understand graph structure, rather than simply translate adjacency matrices into long textual edge lists. A useful graph caption abstracts connectivity into recognizable motifs, such as hubs, paths, cycles, cliques, and bridges, because these motifs provide compact structural units that are easier to read, compare, and recover. In this paper, we study motif-oriented graph captioning as a bidirectional graph-text translation task, where captions must both preserve enough topology for graph recovery and express the graph through concise motif-level descriptions. We show that direct prompting of GPT-5.1 often produces graph-recoverable captions by enumerating node-to-node connections, but these captions are verbose and can contain inconsistent motif interpretations. To address this gap, we introduce Structurally Speaking, a lightweight structured prompting protocol that guides translation between explicit connectivity and motif-level abstraction. Experiments on a synthetic motif-based dataset show that structured prompting produces shorter and more motif-consistent captions while maintaining comparable graph recovery. These results suggest that explicit topology-to-motif reasoning guidance can make LLM-generated graph captions more interpretable without model fine-tuning.", "url": "https://wpnews.pro/news/structurally-speaking-motif-oriented-graph-captioning-through-bidirectional-text", "canonical_source": "https://arxiv.org/abs/2609.10923", "published_at": "2026-09-11 04:00:00+00:00", "updated_at": "2026-09-11 04:26:12.321179+00:00", "lang": "en", "topics": ["large-language-models", "artificial-intelligence", "ai-research", "natural-language-processing"], "entities": ["Structurally Speaking", "GPT-5.1", "arXiv"], "alternates": {"html": "https://wpnews.pro/news/structurally-speaking-motif-oriented-graph-captioning-through-bidirectional-text", "markdown": "https://wpnews.pro/news/structurally-speaking-motif-oriented-graph-captioning-through-bidirectional-text.md", "text": "https://wpnews.pro/news/structurally-speaking-motif-oriented-graph-captioning-through-bidirectional-text.txt", "jsonld": "https://wpnews.pro/news/structurally-speaking-motif-oriented-graph-captioning-through-bidirectional-text.jsonld"}}