Towards Budget-Aware Dense Retrieval for Tables: Trade-offs, Alternatives and Future Directions
Inwon Kang, Kavitha Srinivas, Sola Shirai et al.
· 0 citations
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#machine learning
Apr 2026
Towards Universal Tabular Embeddings: A Benchmark Across Data Tasks
TEmBed is introduced, the Tabular Embedding Test Bed, a unified benchmark for systematically evaluating tabular embeddings across four representation levels: cell, row, column, and table and shows that which model to use depends on the task and representation level.
Liane Vogel, Kavitha Srinivas, N. D'Souza et al.
· arXiv.org · 4 citations
How Much Structure Should Agentic Graph Memory Build for Text Retrieval?
Satyananda Kashyap, Nandana Mihindukulasooriya, Horst Samulowitz
· 0 citations