1 code implementation • 19 Feb 2024 • Shir Lissak, Nitay Calderon, Geva Shenkman, Yaakov Ophir, Eyal Fruchter, Anat Brunstein Klomek, Roi Reichart
Queer youth face increased mental health risks, such as depression, anxiety, and suicidal ideation.
no code implementations • 1 Oct 2023 • Yair Gat, Nitay Calderon, Amir Feder, Alexander Chapanin, Amit Sharma, Roi Reichart
We hence present a second approach based on matching, and propose a method that is guided by an LLM at training-time and learns a dedicated embedding space.
2 code implementations • 31 May 2023 • Nitay Calderon, Naveh Porat, Eyal Ben-David, Alexander Chapanin, Zorik Gekhman, Nadav Oved, Vitaly Shalumov, Roi Reichart
We then conducted a comprehensive large-scale DR study involving over 14, 000 domain shifts across 21 fine-tuned models and few-shot LLMs.
1 code implementation • 3 May 2023 • Nitay Calderon, Subhabrata Mukherjee, Roi Reichart, Amir Kantor
In this work, we study the potential of compressing them, which is crucial for real-world applications serving millions of users.
no code implementations • 19 Feb 2023 • Yael Badian, Yaakov Ophir, Refael Tikochinski, Nitay Calderon, Anat Brunstein Klomek, Roi Reichart
Notably, the study illustrates the advantages of hybrid models in such complicated tasks and provides simple and flexible prediction strategies that could be utilized to develop real-life monitoring tools of suicide.
1 code implementation • 12 Jun 2022 • Itai Gat, Nitay Calderon, Roi Reichart, Tamir Hazan
This work suggests a theoretical framework for model interpretability by measuring the contribution of relevant features to the functional entropy of the network with respect to the input.
no code implementations • 26 Apr 2022 • Roee Shraga, Gil Katz, Yael Badian, Nitay Calderon, Avigdor Gal
In this paper we propose a design methodology, using active learning to enhance learning capabilities, for building a model of production outcome using a constrained amount of raw material training data.
1 code implementation • ACL 2022 • Nitay Calderon, Eyal Ben-David, Amir Feder, Roi Reichart
Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples.