Structured Prompting

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Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation
Sep 10, 2026Hsiao-Ying Lu, Dongyu Liu, Kwan-Liu MaStructured Prompting Prompt Engineering
Department of Computer Science, University of California, Davis
Your Prompt Is Not the Only Prompt: How Much Do LLMs Weight Structured-Output Schema Descriptions?
Aug 8, 2026Sin-Ying LinPrompt Sensitivity Structured Prompting
Independent Researcher
Asking Questions the Right Way: A Multi-Agent Conversational System for Prompt Formulation in Complex Task Resolution
Aug 2, 2026B. Sankar, Pawni Yadav, Srinidhi Ranjini Girish +1Multi-Agent LLM Systems Structured Prompting
Department of Mechanical Engineering, Indian Institute of Science (IISc), Bangalore, India · Department of Design and Manufacturing, Indian Institute of Science (IISc), Bangalore, India
Structured Output Collapses Answer Diversity Across 44 Language Models
Jul 20, 2026Tapan ParikhLLM Evaluation Structured Prompting
Cornell Tech
Narration-of-Thought: Inference-Time Scaffolding for Defeasible Ethical Reasoning in Large Language Models
Jun 24, 2026Patrick Cooper, Alvaro VelasquezStructured Prompting Moral Reasoning in Language Models
Department of Computer Science University of Colorado Boulder
PromptMN: Pseudo Prompting Language
Jun 15, 2026Enkhzol DovdonStructured Prompting LLM Prompting
ICT Group
Capacity, Not Format: Rethinking Structured Reasoning Failures
Jun 8, 2026Hengxin FanLLM Evaluation Structured Prompting
Tianjin Normal University
Less Back-and-Forth: A Comparative Study of Structured Prompting
May 19, 2026Saurav Ghosh, Gabriella Polach, Abdou SowLLM Evaluation Structured Prompting
Washington University in St. Louis Saint Louis, Missouri, USA
Efficient Table QA via TableGrid Navigation and Progressive Inference Prompting
May 18, 2026Amritansh Maurya, Navjot Singh, Mohammed Javed +1Table QA Structured Prompting
Vision Intelligence Lab, IIIT Allahabad, Prayagraj, India · iMeDIA Lab, IIIT Allahabad, Prayagraj, India · CV:HCI Lab, Karlsruhe Institute of Technology, Karlsruhe, Germany
Learning When Not to Decide: A Framework for Overcoming Factual Presumptuousness in AI Adjudication
Apr 21, 2026Mohamed Afane, Emily Robitschek, Derek Ouyang +1AI-Assisted Decision Making Structured Prompting
Stanford University · Stanford, CA, USA
AERMANI-VLM: Structured Prompting and Reasoning for Aerial Manipulation with Vision Language Models
Nov 3, 2025Sarthak Mishra, Rishabh Dev Yadav, Avirup Das +3Aerial Robotics Structured Prompting
Robotics Research Center, IIIT Hyderabad, India · Department of Computer Science, University of Manchester, UK · Newcastle University, UK
Prompting with Sign Parameters for Low-resource Sign Language Instruction Generation
Date pendingMd Tariquzzaman, Md Farhan Ishmam, Saiyma Sittul Muna +2VLM Evaluation Vision-Language Models
Department of Computer Science and Engineering Islamic University of Technology, Bangladesh

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Structurally Speaking: Motif-Oriented Graph Captioning through Bidirectional Graph-Text Translation

Hsiao-Ying Lu, Dongyu Liu, Kwan-Liu Ma

Department of Computer Science, University of California, Davis

Structured Prompting Prompt Engineering LLMs for Graph Learning

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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.

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