John X. Morris

According to our database, John X. Morris authored at least 20 papers between 2020 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

DIRI: Adversarial Patient Reidentification with Large Language Models for Evaluating Clinical Text Anonymization.

John X. Morris, Thomas R. Campion Jr., Sri Laasya Nutheti, Yifan Peng, Akhil Raj, Ramin Zabih, Curtis L. Cole

CoRR, 2024

Contextual Document Embeddings.

John X. Morris, Alexander M. Rush

CoRR, 2024

Soft Prompts Go Hard: Steering Visual Language Models with Hidden Meta-Instructions.

Tingwei Zhang, Collin Zhang, John X. Morris, Eugene Bagdasaryan, Vitaly Shmatikov

CoRR, 2024

Corpus Poisoning via Approximate Greedy Gradient Descent.

Jinyan Su, John X. Morris, Preslav Nakov, Claire Cardie

CoRR, 2024

Crafting Interpretable Embeddings by Asking LLMs Questions.

Vinamra Benara, Chandan Singh, John X. Morris, Richard Antonello, Ion Stoica, Alexander G. Huth, Jianfeng Gao

CoRR, 2024

Do language models plan ahead for future tokens?

Wilson Wu, John X. Morris, Lionel Levine

CoRR, 2024

Nomic Embed: Training a Reproducible Long Context Text Embedder.

Zach Nussbaum, John X. Morris, Brandon Duderstadt, Andriy Mulyar

CoRR, 2024

Language Model Inversion.

John X. Morris, Wenting Zhao, Justin T. Chiu, Vitaly Shmatikov, Alexander M. Rush

Proceedings of the Twelfth International Conference on Learning Representations, 2024

Extracting Prompts by Inverting LLM Outputs.

Collin Zhang, John X. Morris, Vitaly Shmatikov

Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, 2024

2023

Tree Prompting: Efficient Task Adaptation without Fine-Tuning.

Chandan Singh, John X. Morris, Alexander M. Rush, Jianfeng Gao, Yuntian Deng

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Text Embeddings Reveal (Almost) As Much As Text.

John X. Morris, Volodymyr Kuleshov, Vitaly Shmatikov, Alexander M. Rush

Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, 2023

Explaining Data Patterns in Natural Language with Language Models.

Chandan Singh, John X. Morris, Jyoti Aneja, Alexander M. Rush, Jianfeng Gao

Proceedings of the 6th BlackboxNLP Workshop: Analyzing and Interpreting Neural Networks for NLP, 2023

2022

Explaining Patterns in Data with Language Models via Interpretable Autoprompting.

Chandan Singh, John X. Morris, Jyoti Aneja, Alexander M. Rush, Jianfeng Gao

CoRR, 2022

Unsupervised Text Deidentification.

John X. Morris, Justin T. Chiu, Ramin Zabih, Alexander M. Rush

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2022, 2022

2020

TextAttack: Lessons learned in designing Python frameworks for NLP.

John X. Morris, Jin Yong Yoo, Yanjun Qi

CoRR, 2020

TextAttack: A Framework for Adversarial Attacks in Natural Language Processing.

John X. Morris, Eli Lifland, Jin Yong Yoo, Yanjun Qi

CoRR, 2020

TextAttack: A Framework for Adversarial Attacks, Data Augmentation, and Adversarial Training in NLP.

John X. Morris, Eli Lifland, Jin Yong Yoo, Jake Grigsby, Di Jin, Yanjun Qi

Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, 2020

Reevaluating Adversarial Examples in Natural Language.

John X. Morris, Eli Lifland, Jack Lanchantin, Yangfeng Ji, Yanjun Qi

Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2020, 2020

Searching for a Search Method: Benchmarking Search Algorithms for Generating NLP Adversarial Examples.

Jin Yong Yoo, John X. Morris, Eli Lifland, Yanjun Qi

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020

Second-Order NLP Adversarial Examples.

John X. Morris

Proceedings of the Third BlackboxNLP Workshop on Analyzing and Interpreting Neural Networks for NLP, 2020