Hi! My name is Taylor Sorensen. I’m a Computer Science PhD student at the University of Washington, where I’m fortunate to be advised by Yejin Choi. I research natural language processing (NLP) and artificial intelligence (AI) and am especially interested in AI alignment and ethics, large language models, and NLP for social good. I love working with language because I feel it’s the best medium we have for communicating and understanding human intelligence, and I’m passionate about understanding how to make AI/language models work for positive world impact.

Previously, I received my BS in Applied Math and Computer Science at Brigham Young University. I also worked towards an MS, where I worked with David Wingate on a variety of problems ranging from NLP to machine learning to soft robotics until I left to pursue my PhD.

Historically, I’ve worked on a variety of problems ranging from computer vision to RL for soft robotics to ML-based quantitative investing to NLP for drug discovery.

Publications

Publications are listed in reverse chronological order. For a list of all publications, see my google scholar profile.

• A Roadmap to Pluralistic Alignment
  Taylor Sorensen, Jared Moore, Jillian Fisher, Mitchell Gordon, Niloofar Mireshghallah, Christopher Michael Rytting, Andre Ye, Liwei Jiang, Ximing Lu, Nouha Dziri, Tim Althoff, Yejin Choi Arxiv Preprint
  Paper

• Leveraging AI for democratic discourse: Chat interventions can improve online political conversations at scale
  Lisa P. Argyle, Christopher A. Bail, Ethan C. Busby, Joshua R. Gubler, Thomas Howe, Christopher Rytting, Taylor Sorensen, David Wingate
  Published in PNAS
  Paper

• Value Kaleidoscope: Engaging AI with Pluralistic Human Values, Rights, and Duties
  Taylor Sorensen, Liwei Jiang, Jena Hwang, Sydney Levine, Valentina Pyatkin, Peter West, Nouha Dziri, Ximing Lu, Kavel Rao, Chandra Bhagavatula, Maarten Sap, John Tasioulas, Yejin Choi
  AAAI 2024 Oral (top 3% of submissions)
  Paper, Presentation, Demo, Code, Dataset, Model

• NovaCOMET: Open Commonsense Foundation Models with Symbolic Knowledge Distillation
  Peter West, Ronan Bras, Taylor Sorensen, Bill Lin, Liwei Jiang, Ximing Lu, Khyathi Chandu, Jack Hessel, Ashutosh Baheti, Chandra Bhagavatula, Yejin Choi
  Findings of EMNLP 2023
  Paper

• Impossible Distillation: from Low-Quality Model to High-Quality Dataset & Model for Summarization and Paraphrasing
  Jaehun Jung, Peter West, Liwei Jiang, Faeze Brahman, Ximing Lu, Jillian Fisher, Taylor Sorensen, Yejin Choi
  Arxiv Preprint
  Paper

• Towards Coding Social Science Datasets with Language Models
  Christopher Michael Rytting, Taylor Sorensen, Lisa Argyle, Ethan Busby, Nancy Fulda, Joshua Gubler, David Wingate
  Arxiv Preprint
  Paper

• Prompt Compression and Contrastive Conditioning for Controllability and Toxicity Reduction in Language Models
  David Wingate, Mohammad Shoeybi, Taylor Sorensen
  Findings of EMNLP 2022
  Paper, Code

• An Information-theoretic Approach to Prompt Engineering Without Ground Truth Labels
  Taylor Sorensen, Joshua Robinson, Christopher Michael Rytting, Alexander Glenn Shaw, Kyle Jeffrey Rogers, Alexia Pauline Delorey, Mahmoud Khalil, Nancy Fulda, David Wingate
  ACL 2022
  Paper, Code, Presentation

• Nl-augmenter: A framework for task-sensitive natural language augmentation
  Kaustubh D Dhole, Varun Gangal, Sebastian Gehrmann, …, Taylor Sorensen et al.
  Arxiv Preprint
  Paper, Code

• Using first principles for deep learning and model-based control of soft robots
  Curtis C Johnson, Tyler Quackenbush, Taylor Sorensen, David Wingate, Marc D Killpack
  Frontiers in Robotics and AI
  Paper, Code

====== Website last updated: Feb 7, 2024