Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Stanovsky, P"'
Autor:
Dahan, Noam, Stanovsky, Gabriel
Automatic summarization has consistently attracted attention, due to its versatility and wide application in various downstream tasks. Despite its popularity, we find that annotation efforts have largely been disjointed, and have lacked common termin
Externí odkaz:
http://arxiv.org/abs/2411.04585
Autor:
Neuberger, Shlomo, Eckhaus, Niv, Berger, Uri, Taubenfeld, Amir, Stanovsky, Gabriel, Goldstein, Ariel
Many human interactions, such as political debates, are carried out in group settings, where there are arbitrarily many participants, each with different views and agendas. To explore such complex social settings, we present SAUCE: a customizable Pyt
Externí odkaz:
http://arxiv.org/abs/2411.03397
Understanding the inner workings of Transformers is crucial for achieving more accurate and efficient predictions. In this work, we analyze the computation performed by Transformers in the layers after the top-1 prediction has become fixed, which has
Externí odkaz:
http://arxiv.org/abs/2410.20210
The task of image captioning has recently been gaining popularity, and with it the complex task of evaluating the quality of image captioning models. In this work, we present the first survey and taxonomy of over 70 different image captioning metrics
Externí odkaz:
http://arxiv.org/abs/2408.04909
Various tasks, such as summarization, multi-hop question answering, or coreference resolution, are naturally phrased over collections of real-world documents. Such tasks present a unique set of challenges, revolving around the lack of coherent narrat
Externí odkaz:
http://arxiv.org/abs/2406.16086
Few shot in-context learning (ICL) typically assumes access to large annotated training sets. However, in many real world scenarios, such as domain adaptation, there is only a limited budget to annotate a small number of samples, with the goal of max
Externí odkaz:
http://arxiv.org/abs/2406.13274
Most works on gender bias focus on intrinsic bias -- removing traces of information about a protected group from the model's internal representation. However, these works are often disconnected from the impact of such debiasing on downstream applicat
Externí odkaz:
http://arxiv.org/abs/2406.00787
Cross-domain alignment refers to the task of mapping a concept from one domain to another. For example, ``If a \textit{doctor} were a \textit{color}, what color would it be?''. This seemingly peculiar task is designed to investigate how people repres
Externí odkaz:
http://arxiv.org/abs/2405.14863
Autor:
Goldstein, Ariel, Stanovsky, Gabriel
Recent advances in LLMs have sparked a debate on whether they understand text. In this position paper, we argue that opponents in this debate hold different definitions for understanding, and particularly differ in their view on the role of conscious
Externí odkaz:
http://arxiv.org/abs/2403.00499
Document collections of various domains, e.g., legal, medical, or financial, often share some underlying collection-wide structure, which captures information that can aid both human users and structure-aware models. We propose to identify the typica
Externí odkaz:
http://arxiv.org/abs/2402.13906