3 papers
cs.AI2026
From Answers to Interpretations: Rethinking Ambiguity-Induced Aleatoric Uncertainty Estimation in LLMs
Omer Nahum, Niv Nayman, Jonathan Fhima +4
A key challenge in reliable LLM deployment is recognizing when uncertainty reflects irreducible variability in the task rather than limitations in the model's knowledge. In languag…
cs.CL2026
Motivation in Large Language Models
Omer Nahum, Asael Sklar, Ariel Goldstein +1
Motivation is a central driver of human behavior, shaping decisions, goals, and task performance. As large language models (LLMs) become increasingly aligned with human preferences…
cs.CL2024
Are LLMs Better than Reported? Detecting Label Errors and Mitigating Their Effect on Model Performance
Omer Nahum, Nitay Calderon, Orgad Keller +2
NLP benchmarks rely on standardized datasets for training and evaluating models and are crucial for advancing the field. Traditionally, expert annotations ensure high-quality label…