7 citations · 26 across the 11 of their papers we have counts for
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cs.CL2022★ 4 cited
Efficient Long-Text Understanding with Short-Text Models
Maor Ivgi, Uri Shaham, Jonathan Berant
Transformer-based pretrained language models (LMs) are ubiquitous across natural language understanding, but cannot be applied to long sequences such as stories, scientific article…
cs.CL2022
Scaling Laws Under the Microscope: Predicting Transformer Performance from Small Scale Experiments
Maor Ivgi, Yair Carmon, Jonathan Berant
Neural scaling laws define a predictable relationship between a model's parameter count and its performance after training in the form of a power law. However, most research to dat…
cs.CL2022★ 2 cited
SCROLLS: Standardized CompaRison Over Long Language Sequences
Uri Shaham, Elad Segal, Maor Ivgi +8
NLP benchmarks have largely focused on short texts, such as sentences and paragraphs, even though long texts comprise a considerable amount of natural language in the wild. We intr…