Zobrazeno 1 - 10
of 100 140
pro vyhledávání: '"P Levy"'
Precise action localization in untrimmed video is vital for fields such as professional sports and minimally invasive surgery, where the delineation of particular motions in recordings can dramatically enhance analysis. But in many cases, large scale
Externí odkaz:
http://arxiv.org/abs/2410.14340
With the increasing adoption of large language models (LLMs) in education, concerns about inherent biases in these models have gained prominence. We evaluate LLMs for bias in the personalized educational setting, specifically focusing on the models'
Externí odkaz:
http://arxiv.org/abs/2410.14012
We develop a new longitudinal count data regression model that accounts for zero-inflation and spatio-temporal correlation across responses. This project is motivated by an analysis of Iowa Fluoride Study (IFS) data, a longitudinal cohort study with
Externí odkaz:
http://arxiv.org/abs/2410.13949
Autor:
Levy, Amit Arnold, Geva, Mor
Large language models (LLMs) frequently make errors when handling even simple numerical problems, such as comparing two small numbers. A natural hypothesis is that these errors stem from how LLMs represent numbers, and specifically, whether their rep
Externí odkaz:
http://arxiv.org/abs/2410.11781
Autor:
Galatzer-Levy, Isaac R., McGiffin, Jed, Munday, David, Liu, Xin, Karmon, Danny, Labzovsky, Ilia, Moroshko, Rivka, Zait, Amir, McDuff, Daniel
Generative AI's rapid advancement sparks interest in its cognitive abilities, especially given its capacity for tasks like language understanding and code generation. This study explores how several recent GenAI models perform on the Clock Drawing Te
Externí odkaz:
http://arxiv.org/abs/2410.11756
Empowerment has the potential to help agents learn large skillsets, but is not yet a scalable solution for training general-purpose agents. Recent empowerment methods learn diverse skillsets by maximizing the mutual information between skills and sta
Externí odkaz:
http://arxiv.org/abs/2410.11155
Large language models (LLMs) acquire beliefs about gender from training data and can therefore generate text with stereotypical gender attitudes. Prior studies have demonstrated model generations favor one gender or exhibit stereotypes about gender,
Externí odkaz:
http://arxiv.org/abs/2410.11084
Traditionally, model-based reinforcement learning (MBRL) methods exploit neural networks as flexible function approximators to represent a priori unknown environment dynamics. However, training data are typically scarce in practice, and these black-b
Externí odkaz:
http://arxiv.org/abs/2410.09163
In this paper, we establish the global convergence of the actor-critic algorithm with a significantly improved sample complexity of $O(\epsilon^{-3})$, advancing beyond the existing local convergence results. Previous works provide local convergence
Externí odkaz:
http://arxiv.org/abs/2410.08868
Recent advancements in LLM-based web agents have introduced novel architectures and benchmarks showcasing progress in autonomous web navigation and interaction. However, most existing benchmarks prioritize effectiveness and accuracy, overlooking cruc
Externí odkaz:
http://arxiv.org/abs/2410.06703