1 citations · 1 across the 4 of their papers we have counts for
4 papers
HotelQuEST: Balancing Quality and Efficiency in Agentic Search
Guy Hadad, Shadi Iskander, Oren Kalinsky +3
Agentic search has emerged as a promising paradigm for adaptive retrieval systems powered by large language models (LLMs). However, existing benchmarks primarily focus on quality,…
Quality Matters: Evaluating Synthetic Data for Tool-Using LLMs
Shadi Iskander, Nachshon Cohen, Zohar Karnin +2
Training large language models (LLMs) for external tool usage is a rapidly expanding field, with recent research focusing on generating synthetic data to address the shortage of av…
Leveraging Prototypical Representations for Mitigating Social Bias without Demographic Information
Shadi Iskander, Kira Radinsky, Yonatan Belinkov
Mitigating social biases typically requires identifying the social groups associated with each data sample. In this paper, we present DAFair, a novel approach to address social bia…
Shielded Representations: Protecting Sensitive Attributes Through Iterative Gradient-Based Projection
Shadi Iskander, Kira Radinsky, Yonatan Belinkov
Natural language processing models tend to learn and encode social biases present in the data. One popular approach for addressing such biases is to eliminate encoded information f…