Grounded Text Generation

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

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The Copy Ceiling: An Input-Exposure Control for Ontology-Grounded Generation over Curated Corpora
Sep 21, 2026John J. O'HareGrounded Text Generation LLM Evaluation
DreamLab AI, The Loom / VisionFlow neurosymbolic stack
From Visual Feedback to Textual Reviews: A Multi-Agent Vision-Language Framework for Image-Grounded Review Assistance
Sep 13, 2026Utsav Kumar Nareti, Ayush Bansal, Kumari Priya +4Grounded Text Generation VLM Reasoning
Dept. of CSE, Indian Institute of Technology Patna, India-801106 · Dept. of CSE, Indian Institute of Technology Indore, India-453552 · NCMI, CSIRO, Australia-2601 +1
Verifiable Disaster Storylines and Causal Knowledge Graphs: A Citation-Grounded Pipeline from Heterogeneous Humanitarian Sources
Sep 1, 2026Ivan Decostanzi, Michele Ronco, Sergio Consoli +8KG Construction Grounded Text Generation
ISI Foundation · Turin, Italy · European Commission, Joint Research Centre (JRC) +4
FACTWASH: Catching AI Rewrites That Wash Hearsay into Fact
Aug 4, 2026Alex KwonConversational Memory Grounded Text Generation
Independent Researcher
Gaze-to-text Generation: Beyond Categorical Decoding of Human Attention
Jul 27, 2026Sounak Mondal, Dimitris Samaras, Gregory Zelinsky +1Grounded Text Generation
Stony Brook University, Stony Brook, New York, USA · Australian Institute for Machine Learning, Adelaide University, Australia
Token-Level Off-Policy Learning for Faithful Generation Under Distribution Shift
Jul 20, 2026Zitong Huang, Gustavo Lucas Carvalho, Deqing Fu +1Grounded Text Generation OOD Generalization
University of Southern California
From Personas to Plot: Character-Grounded Multi-Agent Story Generation for Long-Form Narratives
Jul 1, 2026Aayush Aluru, Chloe Ho, Muhammad Hammouri +5Grounded Text Generation Multi-Agent LLMs
Pocket FM · Princeton University · University of Michigan +2
Source-Grounded Data Generation for Text-to-JSON Learning
Jun 18, 2026Sunghee Ahn, Guijin Son, Youngjae YuGrounded Text Generation Synthetic Data Generation
1Seoul National University
Broadening Access to Transportation Safety Data with Generative AI: A Schema-Grounded Framework for Spatial Natural Language Queries
May 20, 2026Mahdi Azhdari, Eric J. GonzalesGrounded Text Generation
Department of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, Massachusetts, USA
Grounded Satirical Generation with RAG
May 11, 2026Oona Itkonen, Yuxin Su, Linyao Du +1Grounded Text Generation Text Generation
University of Helsinki
Reward-Decomposed Reinforcement Learning for Immersive Video Role-Playing
May 6, 2026Miao Wang, Yuling Shi, Yijiang Li +8Grounded Text Generation Large Language Model-Based Role-Play Simulation
1Nanjing University · 2Shanghai Jiao Tong University · University of California, San Diego +3
Tree-of-Text: A Tree-based Prompting Framework for Table-to-Text Generation in the Sports Domain
Apr 29, 2026Shang-Hsuan Chiang, Tsan-Tsung Yang, An-Zi Yen +1Grounded Text Generation LLM Prompting
National Yang Ming Chiao Tung University
LatticeBridge: Rare-Event Sequential Inference for Faithful Structured Sequence Synthesis
Apr 22, 2026Faruk Alpay, Bugra KilictasConstrained Decoding Grounded Text Generation
Department of Computer Engineering, Bahcesehir University
Semantic Prompting: Agentic Incremental Narrative Refinement through Spatial Semantic Interaction
Apr 21, 2026Xuxin Tang, Ibrahim Tahmid, Eric Krokos +3Grounded Text Generation Language Model Steering
Virginia Tech Blacksburg, VA, United States · Department of Defense United States
NightFeats @ MMU-RAGent NeurIPS 2025: A Context-Optimized Multi-Agent RAG System for the Text-to-Text Track
Apr 21, 2026Quentin Fever, Naziha AslamMulti-Agent LLM Systems Grounded Text Generation
Team NightFeats
TxSum: User-Centered Ethereum Transaction Understanding with Micro-Level Semantic Grounding
Dec 7, 2025Zifan Peng, Jingyi Zheng, Yule Liu +8Financial Services Grounded Text Generation
1Hong Kong University of Science and Technology (Guangzhou) · 3Independent Researcher · University of North Texas +2

Sep 21, 2026, cs.CLJ/K move · Enter open · S save

The Copy Ceiling: An Input-Exposure Control for Ontology-Grounded Generation over Curated Corpora

John J. O'Hare

DreamLab AI, The Loom / VisionFlow neurosymbolic stack

Grounded Text Generation LLM Evaluation LLM Grounding

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We built a node that grounds a replaceable language model in a maintained ontology corpus, then asked what its successful-looking evaluation could support. Across ten models, grounding raised target-name recall from 0.265 unaided to about 0.92. A copy baseline, the recall a verbatim copy of the shown context already achieves, scores 0.964, and every model sits 0.022 to 0.067 below it. Copying therefore scores higher on this limited recall measure, which does not assess whether answers are better. The comparison tests what a recall score establishes; it does not test whether reasoning occurred, because a reasoned answer and a copy score alike when the answer name is already in context. We report exposure accounting (four counts classifying each gold item by whether the context exposed it and the answer recovered it) and a model-judged audit of 423 sampled item observations. A separate paired production study found a model-judged quality gain of +0.27 [+0.11, +0.45] on a 0-5 scale. Operational studies found failures that recall alone would not show: rephrasing questions out of the graph's vocabulary cut exposure from 0.964 to 0.328, yet the absence-keyed fallback would have fired on only 2 of 506; and inserting extracted facts degraded judged pages in every arm, so that step was disabled. Five-arm controls show that any well-formed on-corpus block beats no context but do not establish that the specific content matters, and no matched comparison against flat-text retrieval was run. The corpus is public and largely LLM-generated, which establishes neither training exposure nor novelty. Each study has its own outcome measure. Where gold derives from the injected corpus, we recommend reporting the accounting beside quality judgements, not in place of them.

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