Clemencia Siro, Laura Hollink A · 2026 · arXiv (Cornell University)
DOI: 10.48550/arxiv.2610.31193 · cited by 0 works (OpenAlex) · open-access PDF
मेटाडेटा-अनुबंधित उत्पत्ति को अक्सर एक डेटा-टेक्स्ट कार्य के रूप में फ्रेम किया जाता है जिसमें अतिरिक्त इनपुट फ़ील्ड जमीन में सुधार करना चाहिए। संग्रहालय वर्णन उत्पत्ति इस धारणा को चुनौती देता है क्योंकि कैटलॉग रिकॉर्ड heterogeneous हैं, आंशिक रूप से दृश्य, और भौगोलिक क्षेत्रों पर अनियमित रूप से दस्तावेज किया जाता है। हम अध्ययन करते हैं कि मेटाडेटा संरचना दो चरण के ढांचे के माध्यम से आर्टिफैक्ट वर्णन के उत्पत्ति को कैसे आकार देता है। चरण 1 एक बड़े संग्रहालय संग्रह में प्राकृतिक मेटाडेटा भिन्नता का विश्लेषण करता है, जहां फ़ील्ड गिनती, फ़ील्ड प्रकार, छ
Machine translation (Meta M2M100); the English original follows.
Metadata-conditioned generation is often framed as a data-to-text task in which additional input fields should improve grounding. Museum description generation challenges this assumption because catalogue records are heterogeneous, partially visual, and unevenly documented across geographic regions. We study how metadata composition shapes the generation of artefact descriptions through a two-phase framework. Phase 1 analyses natural metadata variation in a large museum collection, where field count, field type, image availability, and Place co-vary. Phase 2 introduces controlled field probes that hold object identity fixed while varying metadata quantity, field type, and image access. Our results show that metadata quantity alone does not explain generation behaviour. Field type determines whether models ground, omit, or infer information, while images can both support visual grounding and increase unsupported interpretation. More broadly, our findings show that metadata-conditioned data-to-text evaluation should assess field- and claim-level support, rather than relying only on aggregate input coverage or reference similarity.
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Topics: Metadata, Computer science, Field (mathematics), Information retrieval, Task (project management), Object (grammar)
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