78 citations · 102 across the 15 of their papers we have counts for
18 papers
Multimodal Unlearning Across Vision, Language, Video, and Audio: Survey of Methods, Datasets, and Benchmarks
Nobin Sarwar, Shubhashis Roy Dipta, Zheyuan Liu +1
With the growing adoption of VLMs, DMs, LLMs, and AFMs, these multimodal foundation models can inadvertently encode sensitive, copyrighted, biased, or unsafe cross-modal associatio…
Read, Look or Listen? What's Needed for Solving a Multimodal Dataset
Netta Madvil, Yonatan Bitton, Roy Schwartz
The prevalence of large-scale multimodal datasets presents unique challenges in assessing dataset quality. We propose a two-step method to analyze multimodal datasets, which levera…
Morphosyntactic probing of multilingual BERT models
Judit Acs, Endre Hamerlik, Roy Schwartz +2
We introduce an extensive dataset for multilingual probing of morphological information in language models (247 tasks across 42 languages from 10 families), each consisting of a se…
Finding the SWEET Spot: Analysis and Improvement of Adaptive Inference in Low Resource Settings
Daniel Rotem, Michael Hassid, Jonathan Mamou +1
Adaptive inference is a simple method for reducing inference costs. The method works by maintaining multiple classifiers of different capacities, and allocating resources to each t…
Surveying (Dis)Parities and Concerns of Compute Hungry NLP Research
Ji-Ung Lee, Haritz Puerto, Betty van Aken +8
Many recent improvements in NLP stem from the development and use of large pre-trained language models (PLMs) with billions of parameters. Large model sizes makes computational cos…
Fighting Bias with Bias: Promoting Model Robustness by Amplifying Dataset Biases
Yuval Reif, Roy Schwartz
NLP models often rely on superficial cues known as dataset biases to achieve impressive performance, and can fail on examples where these biases do not hold. Recent work sought to…
VASR: Visual Analogies of Situation Recognition
Yonatan Bitton, Ron Yosef, Eli Strugo +3
A core process in human cognition is analogical mapping: the ability to identify a similar relational structure between different situations. We introduce a novel task, Visual Anal…
TangoBERT: Reducing Inference Cost by using Cascaded Architecture
Jonathan Mamou, Oren Pereg, Moshe Wasserblat +1
The remarkable success of large transformer-based models such as BERT, RoBERTa and XLNet in many NLP tasks comes with a large increase in monetary and environmental cost due to the…
ABC: Attention with Bounded-memory Control
Hao Peng, Jungo Kasai, Nikolaos Pappas +5
Transformer architectures have achieved state-of-the-art results on a variety of sequence modeling tasks. However, their attention mechanism comes with a quadratic complexity in se…
Expected Validation Performance and Estimation of a Random Variable's Maximum
Jesse Dodge, Suchin Gururangan, Dallas Card +2
Research in NLP is often supported by experimental results, and improved reporting of such results can lead to better understanding and more reproducible science. In this paper we…
Data Efficient Masked Language Modeling for Vision and Language
Yonatan Bitton, Gabriel Stanovsky, Michael Elhadad +1
Masked language modeling (MLM) is one of the key sub-tasks in vision-language pretraining. In the cross-modal setting, tokens in the sentence are masked at random, and the model pr…
Effects of Parameter Norm Growth During Transformer Training: Inductive Bias from Gradient Descent
William Merrill, Vivek Ramanujan, Yoav Goldberg +2
The capacity of neural networks like the widely adopted transformer is known to be very high. Evidence is emerging that they learn successfully due to inductive bias in the trainin…
RNN Architecture Learning with Sparse Regularization
Jesse Dodge, Roy Schwartz, Hao Peng +1
Neural models for NLP typically use large numbers of parameters to reach state-of-the-art performance, which can lead to excessive memory usage and increased runtime. We present a…
PaLM: A Hybrid Parser and Language Model
Hao Peng, Roy Schwartz, Noah A. Smith
We present PaLM, a hybrid parser and neural language model. Building on an RNN language model, PaLM adds an attention layer over text spans in the left context. An unsupervised con…
Green AI
Roy Schwartz, Jesse Dodge, Noah A. Smith +1
The computations required for deep learning research have been doubling every few months, resulting in an estimated 300,000x increase from 2012 to 2018 [2]. These computations have…
Rational Recurrences
Hao Peng, Roy Schwartz, Sam Thomson +1
Despite the tremendous empirical success of neural models in natural language processing, many of them lack the strong intuitions that accompany classical machine learning approach…
LSTMs Exploit Linguistic Attributes of Data
Nelson F. Liu, Omer Levy, Roy Schwartz +2
While recurrent neural networks have found success in a variety of natural language processing applications, they are general models of sequential data. We investigate how the prop…
Automatic Selection of Context Configurations for Improved Class-Specific Word Representations
Ivan Vulić, Roy Schwartz, Ari Rappoport +2
This paper is concerned with identifying contexts useful for training word representation models for different word classes such as adjectives (A), verbs (V), and nouns (N). We int…