activity
20172024
most citedLow Latency Privacy Preserving Inference

52 citations · 85 across the 8 of their papers we have counts for

collaborators

8 papers

cs.DB2024★ 4 cited

ReMatch: Retrieval Enhanced Schema Matching with LLMs

Eitam Sheetrit, Menachem Brief, Moshik Mishaeli +1

Schema matching is a crucial task in data integration, involving the alignment of a source schema with a target schema to establish correspondence between their elements. This task…

cs.CL2024

Evaluation Methodology for Large Language Models for Multilingual Document Question and Answer

Adar Kahana, Jaya Susan Mathew, Said Bleik +2

With the widespread adoption of Large Language Models (LLMs), in this paper we investigate the multilingual capability of these models. Our preliminary results show that, translati…

cs.AI2024★ 27 cited

Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs

Oded Ovadia, Menachem Brief, Moshik Mishaeli +1

Large language models (LLMs) encapsulate a vast amount of factual information within their pre-trained weights, as evidenced by their ability to answer diverse questions across dif…

cs.LG2023

Predicting Unplanned Readmissions in the Intensive Care Unit: A Multimodality Evaluation

Eitam Sheetrit, Menachem Brief, Oren Elisha

A hospital readmission is when a patient who was discharged from the hospital is admitted again for the same or related care within a certain period. Hospital readmissions are a si…

cs.LG2023

MessageNet: Message Classification using Natural Language Processing and Meta-data

Adar Kahana, Oren Elisha

In this paper we propose a new Deep Learning (DL) approach for message classification. Our method is based on the state-of-the-art Natural Language Processing (NLP) building blocks…

cs.LG2018★ 52 cited

Low Latency Privacy Preserving Inference

Alon Brutzkus, Oren Elisha, Ran Gilad-Bachrach

When applying machine learning to sensitive data, one has to find a balance between accuracy, information security, and computational-complexity. Recent studies combined Homomorphi…