37 citations · 243 across the 44 of their papers we have counts for
16 papers · 1 filter
Lexical Generalization Improves with Larger Models and Longer Training
Elron Bandel, Yoav Goldberg, Yanai Elazar
While fine-tuned language models perform well on many tasks, they were also shown to rely on superficial surface features such as lexical overlap. Excessive utilization of such heu…
DALLE-2 is Seeing Double: Flaws in Word-to-Concept Mapping in Text2Image Models
Royi Rassin, Shauli Ravfogel, Yoav Goldberg
We study the way DALLE-2 maps symbols (words) in the prompt to their references (entities or properties of entities in the generated image). We show that in stark contrast to the w…
CIKQA: Learning Commonsense Inference with a Unified Knowledge-in-the-loop QA Paradigm
Hongming Zhang, Yintong Huo, Yanai Elazar +3
Recently, the community has achieved substantial progress on many commonsense reasoning benchmarks. However, it is still unclear what is learned from the training process: the know…
Log-linear Guardedness and its Implications
Shauli Ravfogel, Yoav Goldberg, Ryan Cotterell
Methods for erasing human-interpretable concepts from neural representations that assume linearity have been found to be tractable and useful. However, the impact of this removal o…
Understanding Transformer Memorization Recall Through Idioms
Adi Haviv, Ido Cohen, Jacob Gidron +3
To produce accurate predictions, language models (LMs) must balance between generalization and memorization. Yet, little is known about the mechanism by which transformer LMs emplo…
Measuring Causal Effects of Data Statistics on Language Model's `Factual' Predictions
Yanai Elazar, Nora Kassner, Shauli Ravfogel +6
Large amounts of training data are one of the major reasons for the high performance of state-of-the-art NLP models. But what exactly in the training data causes a model to make a…