63 citations · 269 across the 51 of their papers we have counts for
59 papers
Multilinguality at the Edge: Developing Language Models for the Global South
Lester James V. Miranda, Songbo Hu, Roi Reichart +1
Where and how language models (LMs) are deployed determines who can benefit from them. However, there are several challenges that prevent effective deployment of LMs in non-English…
Navigating Cultural Chasms: Exploring and Unlocking the Cultural POV of Text-To-Image Models
Mor Ventura, Eyal Ben-David, Anna Korhonen +1
Text-To-Image (TTI) models, such as DALL-E and StableDiffusion, have demonstrated remarkable prompt-based image generation capabilities. Multilingual encoders may have a substantia…
Measuring the Robustness of NLP Models to Domain Shifts
Nitay Calderon, Naveh Porat, Eyal Ben-David +5
Existing research on Domain Robustness (DR) suffers from disparate setups, limited task variety, and scarce research on recent capabilities such as in-context learning. Furthermore…
A Systematic Study of Knowledge Distillation for Natural Language Generation with Pseudo-Target Training
Nitay Calderon, Subhabrata Mukherjee, Roi Reichart +1
Modern Natural Language Generation (NLG) models come with massive computational and storage requirements. In this work, we study the potential of compressing them, which is crucial…
Human Choice Prediction in Language-based Persuasion Games: Simulation-based Off-Policy Evaluation
Eilam Shapira, Omer Madmon, Reut Apel +2
Recent advances in Large Language Models (LLMs) have spurred interest in designing LLM-based agents for tasks that involve interaction with human and artificial agents. This paper…
A Picture May Be Worth a Thousand Lives: An Interpretable Artificial Intelligence Strategy for Predictions of Suicide Risk from Social Media Images
Yael Badian, Yaakov Ophir, Refael Tikochinski +3
The promising research on Artificial Intelligence usages in suicide prevention has principal gaps, including black box methodologies, inadequate outcome measures, and scarce resear…
Domain Adaptation from Scratch
Eyal Ben-David, Yftah Ziser, Roi Reichart
Natural language processing (NLP) algorithms are rapidly improving but often struggle when applied to out-of-distribution examples. A prominent approach to mitigate the domain gap…
Multi-task Active Learning for Pre-trained Transformer-based Models
Guy Rotman, Roi Reichart
Multi-task learning, in which several tasks are jointly learned by a single model, allows NLP models to share information from multiple annotations and may facilitate better predic…
A Functional Information Perspective on Model Interpretation
Itai Gat, Nitay Calderon, Roi Reichart +1
Contemporary predictive models are hard to interpret as their deep nets exploit numerous complex relations between input elements. This work suggests a theoretical framework for mo…
On the Robustness of Dialogue History Representation in Conversational Question Answering: A Comprehensive Study and a New Prompt-based Method
Zorik Gekhman, Nadav Oved, Orgad Keller +2
Most works on modeling the conversation history in Conversational Question Answering (CQA) report a single main result on a common CQA benchmark. While existing models show impress…
In the Eye of the Beholder: Robust Prediction with Causal User Modeling
Amir Feder, Guy Horowitz, Yoav Wald +2
Accurately predicting the relevance of items to users is crucial to the success of many social platforms. Conventional approaches train models on logged historical data; but recomm…
Learning Discrete Structured Variational Auto-Encoder using Natural Evolution Strategies
Alon Berliner, Guy Rotman, Yossi Adi +2
Discrete variational auto-encoders (VAEs) are able to represent semantic latent spaces in generative learning. In many real-life settings, the discrete latent space consists of hig…
CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior
Eldar David Abraham, Karel D'Oosterlinck, Amir Feder +5
The increasing size and complexity of modern ML systems has improved their predictive capabilities but made their behavior harder to explain. Many techniques for model explanation…
DoCoGen: Domain Counterfactual Generation for Low Resource Domain Adaptation
Nitay Calderon, Eyal Ben-David, Amir Feder +1
Natural language processing (NLP) algorithms have become very successful, but they still struggle when applied to out-of-distribution examples. In this paper we propose a controlla…
Example-based Hypernetworks for Out-of-Distribution Generalization
Tomer Volk, Eyal Ben-David, Ohad Amosy +2
As Natural Language Processing (NLP) algorithms continually achieve new milestones, out-of-distribution generalization remains a significant challenge. This paper addresses the iss…
Causal Inference in Natural Language Processing: Estimation, Prediction, Interpretation and Beyond
Amir Feder, Katherine A. Keith, Emaad Manzoor +10
A fundamental goal of scientific research is to learn about causal relationships. However, despite its critical role in the life and social sciences, causality has not had the same…
DILBERT: Customized Pre-Training for Domain Adaptation withCategory Shift, with an Application to Aspect Extraction
Entony Lekhtman, Yftah Ziser, Roi Reichart
The rise of pre-trained language models has yielded substantial progress in the vast majority of Natural Language Processing (NLP) tasks. However, a generic approach towards the pr…
Towards Zero-shot Language Modeling
Edoardo Maria Ponti, Ivan Vulić, Ryan Cotterell +2
Can we construct a neural model that is inductively biased towards learning human languages? Motivated by this question, we aim at constructing an informative prior over neural wei…
Are VQA Systems RAD? Measuring Robustness to Augmented Data with Focused Interventions
Daniel Rosenberg, Itai Gat, Amir Feder +1
Deep learning algorithms have shown promising results in visual question answering (VQA) tasks, but a more careful look reveals that they often do not understand the rich signal th…
Designing an Automatic Agent for Repeated Language based Persuasion Games
Maya Raifer, Guy Rotman, Reut Apel +2
Persuasion games are fundamental in economics and AI research and serve as the basis for important applications. However, work on this setup assumes communication with stylized mes…
PADA: Example-based Prompt Learning for on-the-fly Adaptation to Unseen Domains
Eyal Ben-David, Nadav Oved, Roi Reichart
Natural Language Processing algorithms have made incredible progress, but they still struggle when applied to out-of-distribution examples. We address a challenging and underexplor…
Combining Deep Generative Models and Multi-lingual Pretraining for Semi-supervised Document Classification
Yi Zhu, Ehsan Shareghi, Yingzhen Li +2
Semi-supervised learning through deep generative models and multi-lingual pretraining techniques have orchestrated tremendous success across different areas of NLP. Nonetheless, th…
Model Compression for Domain Adaptation through Causal Effect Estimation
Guy Rotman, Amir Feder, Roi Reichart
Recent improvements in the predictive quality of natural language processing systems are often dependent on a substantial increase in the number of model parameters. This has led t…
A Closer Look at Few-Shot Crosslingual Transfer: The Choice of Shots Matters
Mengjie Zhao, Yi Zhu, Ehsan Shareghi +4
Few-shot crosslingual transfer has been shown to outperform its zero-shot counterpart with pretrained encoders like multilingual BERT. Despite its growing popularity, little to no…