2 papers
cs.AI2026
CONE: Embeddings for Complex Numerical Data Preserving Unit and Variable Semantics
Gyanendra Shrestha, Anna Pyayt, Michael Gubanov
Large pre-trained models (LMs) and Large Language Models (LLMs) are typically effective at capturing language semantics and contextual relationships. However, these models encounte…
cs.CL2025
Tabular Embeddings for Tables with Bi-Dimensional Hierarchical Metadata and Nesting
Gyanendra Shrestha, Chutain Jiang, Sai Akula +3
Embeddings serve as condensed vector representations for real-world entities, finding applications in Natural Language Processing (NLP), Computer Vision, and Data Management across…