Jesse Vig | DeepAI

AI Chat

Featured Co-authors

Caiming Xiong
210 publications
Mohit Bansal
184 publications
Christopher Ré
125 publications
Shafiq Joty
118 publications
Richard Socher
111 publications
Lav R. Varshney
66 publications
Yonatan Belinkov
66 publications
Chien-Sheng Wu
46 publications
Sebastian Gehrmann
38 publications
Alexander R. Fabbri
27 publications
Stephan Zheng
23 publications

∙ 06/01/2023

Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning

Large language models (LLMs) have shown impressive performance in follow...

0 Fan Yin, et al. ∙

∙ 05/30/2023

SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages

Text simplification research has mostly focused on sentence-level simpli...

0 Philippe Laban, et al. ∙

∙ 11/11/2022

Improving Factual Consistency in Summarization with Compression-Based Post-Editing

State-of-the-art summarization models still struggle to be factually con...

0 Alexander R. Fabbri, et al. ∙

∙ 05/05/2022

Interactive Model Cards: A Human-Centered Approach to Model Documentation

Deep learning models for natural language processing (NLP) are increasin...

9 Anamaria Crisan, et al. ∙

∙ 03/08/2022

iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models

Error analysis in NLP models is essential to successful model developmen...

9 Jun Yuan, et al. ∙

∙ 12/14/2021

Exploring Neural Models for Query-Focused Summarization

Query-focused summarization (QFS) aims to produce summaries that answer ...

7 Jesse Vig, et al. ∙

∙ 10/14/2021

MoFE: Mixture of Factual Experts for Controlling Hallucinations in Abstractive Summarization

Neural abstractive summarization models are susceptible to generating fa...

5 Prafulla Kumar Choubey, et al. ∙

∙ 04/15/2021

SummVis: Interactive Visual Analysis of Models, Data, and Evaluation for Text Summarization

Novel neural architectures, training strategies, and the availability of...

21 Jesse Vig, et al. ∙

∙ 01/13/2021

Robustness Gym: Unifying the NLP Evaluation Landscape

Despite impressive performance on standard benchmarks, deep neural netwo...

2 Karan Goel, et al. ∙

∙ 12/01/2020

Profile Prediction: An Alignment-Based Pre-Training Task for Protein Sequence Models

For protein sequence datasets, unlabeled data has greatly outpaced label...

6 Pascal Sturmfels, et al. ∙

∙ 06/26/2020

BERTology Meets Biology: Interpreting Attention in Protein Language Models

Transformer architectures have proven to learn useful representations fo...

0 Jesse Vig, et al. ∙

∙ 04/26/2020

Causal Mediation Analysis for Interpreting Neural NLP: The Case of Gender Bias

Common methods for interpreting neural models in natural language proces...

0 Jesse Vig, et al. ∙

∙ 06/12/2019

A Multiscale Visualization of Attention in the Transformer Model

The Transformer is a sequence model that forgoes traditional recurrent a...

0 Jesse Vig, et al. ∙

∙ 06/07/2019

Analyzing the Structure of Attention in a Transformer Language Model

The Transformer is a fully attention-based alternative to recurrent netw...

0 Jesse Vig, et al. ∙

∙ 04/04/2019

Visualizing Attention in Transformer-Based Language Representation Models

We present an open-source tool for visualizing multi-head self-attention...

0 Jesse Vig, et al. ∙

∙ 04/04/2019

Visualizing Attention in Transformer-Based Language models

We present an open-source tool for visualizing multi-head self-attention...

0 Jesse Vig, et al. ∙

Are you a researcher?
Expose your work
to one of the largest
A.I. communities in the world