96 citations · 181 across the 6 of their papers we have counts for
7 papers
Unsupervised Text Deidentification
John X. Morris, Justin T. Chiu, Ramin Zabih +1
Deidentification seeks to anonymize textual data prior to distribution. Automatic deidentification primarily uses supervised named entity recognition from human-labeled data points…
Explaining Patterns in Data with Language Models via Interpretable Autoprompting
Chandan Singh, John X. Morris, Jyoti Aneja +2
Large language models (LLMs) have displayed an impressive ability to harness natural language to perform complex tasks. In this work, we explore whether we can leverage this learne…
Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples
Jin Yong Yoo, John X. Morris, Eli Lifland +1
We study the behavior of several black-box search algorithms used for generating adversarial examples for natural language processing (NLP) tasks. We perform a fine-grained analysi…
Second-Order NLP Adversarial Examples
John X. Morris
Adversarial example generation methods in NLP rely on models like language models or sentence encoders to determine if potential adversarial examples are valid. In these methods, a…
TextAttack: Lessons learned in designing Python frameworks for NLP
John X. Morris, Jin Yong Yoo, Yanjun Qi
TextAttack is an open-source Python toolkit for adversarial attacks, adversarial training, and data augmentation in NLP. TextAttack unites 15+ papers from the NLP adversarial attac…
TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP
John X. Morris, Eli Lifland, Jin Yong Yoo +3
While there has been substantial research using adversarial attacks to analyze NLP models, each attack is implemented in its own code repository. It remains challenging to develop…