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 • 29 Jun 2022 • Zorik Gekhman, Nadav Oved, Orgad Keller, Idan Szpektor, Roi Reichart
We find that high benchmark scores do not necessarily translate to strong robustness, and that various methods can perform extremely differently under different settings.
no code implementations • ACL 2021 • Nadav Oved, Ran Levy
We propose the PASS system (Perturb-and-Select Summarizer) that employs a large pre-trained Transformer-based model (T5 in our case), which follows a few-shot fine-tuning scheme.
1 code implementation • 24 Feb 2021 • Eyal Ben-David, Nadav Oved, Roi Reichart
We address a challenging and underexplored version of this domain adaptation problem, where an algorithm is trained on several source domains, and then applied to examples from unseen domains that are unknown at training time.
1 code implementation • CL (ACL) 2021 • Amir Feder, Nadav Oved, Uri Shalit, Roi Reichart
Concretely, we show that by carefully choosing auxiliary adversarial pre-training tasks, language representation models such as BERT can effectively learn a counterfactual representation for a given concept of interest, and be used to estimate its true causal effect on model performance.
1 code implementation • 24 Dec 2019 • Gal Cohensius, Reshef Meir, Nadav Oved, Roni Stern
We present a Spades bidding algorithm that is superior to recreational human players and to publicly available bots.
2 code implementations • CL (ACL) 2020 • Nadav Oved, Amir Feder, Roi Reichart
We find that our best performing textual model is most associated with topics that are intuitively related to each prediction task and that better models yield higher correlation with more informative topics.