Kavitha Srinivas

BOReFT: Manifold Steering of Language Models for Black-box Optimization

Language models are increasingly used as proposal models for black-box search, from program optimization to molecular design. Existing approaches typically improve proposals through iterative prompting or parameter updates, offering limited control over how completely and efficiently the model's search space is explore...

Towards Budget-Aware Dense Retrieval for Tables: Trade-offs, Alternatives and Future Directions

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.