Evaluating locality in NMT models. - Itay Itzhak; Koustuv Sinha; Brenden M. Lake; Adina Williams; Dieuwke Hupkes (CogSci, 2022)

Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens. - Itay Itzhak; Omer Levy (NAACL-HLT, 2022)

Instructed to Bias: Instruction-Tuned Language Models Exhibit Emergent Cognitive Bias. - Itay Itzhak; Gabriel Stanovsky; Nir Rosenfeld; Yonatan Belinkov (CoRR, 2023)

DOVE: A Large-Scale Multi-Dimensional Predictions Dataset Towards Meaningful LLM Evaluation. - Eliya Habba; Ofir Arviv; Itay Itzhak; Yotam Perlitz; Elron Bandel; Leshem Choshen; Michal Shmueli-Scheuer; Gabriel Stanovsky (ACL (Findings), 2025)

Trust Me, I'm Wrong: High-Certainty Hallucinations in LLMs. - Adi Simhi; Itay Itzhak; Fazl Barez; Gabriel Stanovsky; Yonatan Belinkov (CoRR, 2025)

Planted in Pretraining, Swayed by Finetuning: A Case Study on the Origins of Cognitive Biases in LLMs. - Itay Itzhak; Yonatan Belinkov; Gabriel Stanovsky (CoRR, 2025)