101 citations · 261 across the 12 of their papers we have counts for
15 papers
Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning
Fan Yin, Jesse Vig, Philippe Laban +3
Large language models (LLMs) have shown impressive performance in following natural language instructions to solve unseen tasks. However, it remains unclear whether models truly un…
SWiPE: A Dataset for Document-Level Simplification of Wikipedia Pages
Philippe Laban, Jesse Vig, Wojciech Kryscinski +3
Text simplification research has mostly focused on sentence-level simplification, even though many desirable edits - such as adding relevant background information or reordering co…
Improving Factual Consistency in Summarization with Compression-Based Post-Editing
Alexander R. Fabbri, Prafulla Kumar Choubey, Jesse Vig +2
State-of-the-art summarization models still struggle to be factually consistent with the input text. A model-agnostic way to address this problem is post-editing the generated summ…
Interactive Model Cards: A Human-Centered Approach to Model Documentation
Anamaria Crisan, Margaret Drouhard, Jesse Vig +1
Deep learning models for natural language processing (NLP) are increasingly adopted and deployed by analysts without formal training in NLP or machine learning (ML). However, the d…
iSEA: An Interactive Pipeline for Semantic Error Analysis of NLP Models
Jun Yuan, Jesse Vig, Nazneen Rajani
Error analysis in NLP models is essential to successful model development and deployment. One common approach for diagnosing errors is to identify subpopulations in the dataset whe…
Exploring Neural Models for Query-Focused Summarization
Jesse Vig, Alexander R. Fabbri, Wojciech Kryściński +2
Query-focused summarization (QFS) aims to produce summaries that answer particular questions of interest, enabling greater user control and personalization. While recently released…