27 citations · 32 across the 6 of their papers we have counts for
6 papers
Knowledge-Instruct: Effective Continual Pre-training from Limited Data using Instructions
Oded Ovadia, Meni Brief, Rachel Lemberg +1
While Large Language Models (LLMs) acquire vast knowledge during pre-training, they often lack domain-specific, new, or niche information. Continual pre-training (CPT) attempts to…
SECQUE: A Benchmark for Evaluating Real-World Financial Analysis Capabilities
Noga Ben Yoash, Meni Brief, Oded Ovadia +4
We introduce SECQUE, a comprehensive benchmark for evaluating large language models (LLMs) in financial analysis tasks. SECQUE comprises 565 expert-written questions covering SEC f…
Mixing It Up: The Cocktail Effect of Multi-Task Fine-Tuning on LLM Performance -- A Case Study in Finance
Meni Brief, Oded Ovadia, Gil Shenderovitz +3
The application of large language models (LLMs) in domain-specific contexts, including finance, has expanded rapidly. Domain-specific LLMs are typically evaluated based on their pe…
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…
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…
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…