10 papers
QASP: Query-Adaptive Robust Vector Search Policy
Hakan Ferhatosmanoglu, Kushal Kumar, Tal Wagner +1
A fundamental challenge of vector search is achieving consistently high recall while minimizing computational costs. Fixed search parameters cause significant performance variance…
Frustratingly Simple Black-Box Adaptation of Language Models via Logit Bias
Ofek I. Cohen, Lior Shani, Aviv Rosenberg +3
Many organizations aim to adapt language models for internal use, both to improve performance on domain-specific tasks and to address privacy concerns around sensitive data. Howeve…
Positional LSH: Binary Block Matrix Approximation for Attention with Linear Biases
Daniel Wolfson, Tal Wagner
Positional encoding in transformers is commonly implemented through positional embeddings, attention masks, or bias terms, but formal connections between these mechanisms remain li…
New Bounds for Kernel Sums via Fast Spherical Embeddings
Tal Wagner
We study query time bounds for the fundamental problem of estimating the kernel mean of a query in a finite dataset $X\subset\mathbb{R…
Quantization for Vector Search under Streaming Updates
Ishaq Aden-Ali, Hakan Ferhatosmanoglu, Alexander Greaves-Tunnell +2
Large-scale vector databases for approximate nearest neighbor (ANN) search typically store a quantized dataset in main memory for fast access, and full precision data on remote dis…
Graph-based Nearest Neighbors with Dynamic Updates via Random Walks
Nina Mishra, Yonatan Naamad, Tal Wagner +1
Approximate nearest neighbor search (ANN) is a common way to retrieve relevant search results, especially now in the context of large language models and retrieval augmented genera…