Kaitlyn Zhou
Assistant Professor at Cornell University
Senior AI Researcher at Together AI
kaitlynz@cornell.edu
Bio
I'm an Assistant Professor in Cornell Information Science with field appointment in Computer Science. I'm recruiting PhD students in NLP and HCI in both computer science and information science! If you're interested, list my name in your statement of purpose!
I research the dynamics of human interaction with language models (human-LM interaction), focusing on how generated language shapes human decision-making, reliance, and trust. My research 1) identifies model overconfidence as a key risk of human-LM interactions [EMNLP'23, ACL'24], 2) builds context-aware evaluation frameworks for emergent human-LM interactions [NAACL'25], and 3) reimagines human-LM interactions for historically marginalized user groups via human-centered task ideation [ACL '26].
I graduated with a Ph.D. in Computer Science from Stanford University, advised by Dan Jurafsky. Prior to Stanford, I served on the University of Washington Board of Regents as appointed by Governor Jay Inslee. I studied Computer Science and Human Centered Design and Engineering at the University of Washington (B.Sc., B.Se., M.S.) and have spent summers at Microsoft Research FATE and AI2.
Research Group
PhD Students
Interns
Undergrads
Recent News
- Fall 2026: Upcoming invited talks at the Cornell Global AI Initiative, MIT (EECS), and MilaNLP!
- Fall 2026: Received a Thinking Machines Interactivity Grant!
- September 2026: Invited talk at UMass Amherst (Machine Learning and Friends Lunch)!
- September 2026: Our paper on voice cloning (Voice "Cloning" is Style Transfer) was accepted to NeurIPS 2026!
- Fall 2026: Welcomed two new students, Akriti Jain and Andrew Shaw!
- May 2026: Two papers (Attention to Non-Adopters and ReasonIF) accepted to ACL 2026 Findings!
- April 2026: Invited talk at UC Berkeley!
- February 2026: Preprint on speech model transcription errors of street names ("Sorry, I Didn't Catch That": How Speech Models Miss What Matters Most)
Publications
Most recent publications on Google Scholar.
Voice "Cloning" is Style Transfer
Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds, Anna Pot, Yongchan Kwon, and James Zou
NeurIPS'26: Conference on Neural Information Processing Systems. 2026.
Humans overrely on overconfident language models, across languages
Neil Rathi, Dan Jurafsky, and Kaitlyn Zhou
COLM 2025: Conference on Language Modeling. 2025.
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance Best Paper Runner-Up
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, Nouha Dziri, Dan Jurafsky, and Maarten Sap
NAACL'25: North American Chapter of the Association for Computational Linguistics. 2025.
Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, and Maarten Sap
ACL'24: Annual Meeting of the Association for Computational Linguistics. 2024.
Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto
EMNLP'23: Empirical Methods in Natural Language Processing. 2023.
Spotlight Tweets: A Lens for Exploring Attention Dynamics within Online Sensemaking during Crisis Events
Kaitlyn Zhou, Tom Wilson, Kate Starbird, and Emma S. Spiro
TSC'23: Transactions of Social Computing (Journal). 2023
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu.
NAACL'22: North American Chapter of the Association for Computational Linguistics. 2022
The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality.
Mitchell L. Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori Hashimoto, and Michael S. Bernstein.
CHI'21. ACM Conference on Human Factors in Computing Systems. 2021
Voice "Cloning" is Style Transfer
Kaitlyn Zhou, Federico Bianchi, Martijn Bartelds, Anna Pot, Yongchan Kwon, and James Zou
NeurIPS'26: Conference on Neural Information Processing Systems. 2026.
Greater Accessibility Can Amplify Discrimination in Generative AI
Carolin Holtermann, Minh Duc Bui, Kaitlyn Zhou, Valentin Hofmann, Katharina von der Wense, and Anne Lauscher
arXiv preprint arXiv:2603.22260. 2026.
"Sorry, I Didn't Catch That": How Speech Models Miss What Matters Most
Kaitlyn Zhou, Martijn Bartelds, Federico Bianchi, and James Zou
arXiv preprint arXiv:2602.12249. 2026.
Attention to Non-Adopters
Kaitlyn Zhou, Kristina Gligorić, Myra Cheng, Michelle S. Lam, Vyoma Raman, Boluwatife Aminu, Caeley Woo, Michael Brockman, Hannah Cha, and Dan Jurafsky
ACL'26 (Findings): Association for Computational Linguistics. 2026.
ReasonIF: Large Reasoning Models Fail to Follow Instructions During Reasoning
Yongchan Kwon, Shang Zhu, Federico Bianchi, Kaitlyn Zhou, and James Zou
ACL'26 (Findings): Association for Computational Linguistics. 2026.
Humans overrely on overconfident language models, across languages
Neil Rathi, Dan Jurafsky, and Kaitlyn Zhou
COLM 2025: Conference on Language Modeling. 2025.
Not Like Us, Hunty: Measuring Perceptions and Behavioral Effects of Minoritized Anthropomorphic Cues in LLMs
Jeffrey Basoah, Daniel Chechelnitsky, Tao Long, Katharina Reinecke, Chrysoula Zerva, Kaitlyn Zhou, Mark Díaz, and Maarten Sap
FAccT'25: ACM Conference on Fairness, Accountability, and Transparency. 2025.
ELI-Why: Evaluating the Pedagogical Utility of Language Model Explanations
Brihi Joshi, Keyu He, Sahana Ramnath, Sadra Sabouri, Kaitlyn Zhou, Souti Chattopadhyay, Swabha Swayamdipta, and Xiang Ren
ACL'25 (Findings): Association for Computational Linguistics. 2025.
Rel-A.I.: An Interaction-Centered Approach To Measuring Human-LM Reliance Best Paper Runner-Up
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, Nouha Dziri, Dan Jurafsky, and Maarten Sap
NAACL'25: North American Chapter of the Association for Computational Linguistics. 2025.
Rethinking Word Similarity: Semantic Similarity through Classification Confusion
Kaitlyn Zhou, Haishan Gao, Sarah Chen, Dan Edelstein, Dan Jurafsky, Chen Shani
NAACL'25 (Oral) North American Chapter of the Association for Computational Linguistics. 2025.
Relying on the Unreliable: The Impact of Language Models' Reluctance to Express Uncertainty
Kaitlyn Zhou, Jena D. Hwang, Xiang Ren, and Maarten Sap
ACL'24: Annual Meeting of the Association for Computational Linguistics. 2024.
Navigating the Grey Area: Expressions of Overconfidence and Uncertainty in Language Models
Kaitlyn Zhou, Dan Jurafsky, and Tatsunori Hashimoto
EMNLP'23: Empirical Methods in Natural Language Processing. 2023.
Spotlight Tweets: A Lens for Exploring Attention Dynamics within Online Sensemaking during Crisis Events
Kaitlyn Zhou, Tom Wilson, Kate Starbird, and Emma S. Spiro
TSC'23: Transactions of Social Computing (Journal). 2023
Deconstructing NLG Evaluation: Evaluation Practices, Assumptions, and Their Implications
Kaitlyn Zhou, Su Lin Blodgett, Adam Trischler, Hal Daumé III, Kaheer Suleman, Alexandra Olteanu.
NAACL'22: North American Chapter of the Association for Computational Linguistics. 2022
Problems with Cosine as a Measure of Embedding Similarity for High Frequency Words
Kaitlyn Zhou, Kawin Ethayarajh, Dallas Card, Dan Jurafsky
ACL'22: Annual Meeting of the Association for Computational Linguistics. 2022.
Richer Countries and Richer Representations
Kaitlyn Zhou, Kawin Ethayarajh, Dan Jurafsky
ACL'22: Annual Meeting of the Association for Computational Linguistics (Findings). 2022.
On the Opportunities and Risks of Foundation Models
Rishi Bommasani, Drew A. Hudson ... Kaitlyn Zhou , Percy Liang et al.
Preprint
The Disagreement Deconvolution: Bringing Machine Learning Performance Metrics In Line With Reality.
Mitchell L. Gordon, Kaitlyn Zhou, Kayur Patel, Tatsunori Hashimoto, and Michael S. Bernstein.
CHI'21. ACM Conference on Human Factors in Computing Systems. 2021
Assembling strategic narratives: Information operations as collaborative work within an online community.
Tom Wilson, Kaitlyn Zhou, and Kate Starbird
CSCW'2018: ACM SIGCHI Conference on Computer-Supported Cooperative Work & Social Computing
Centralized, parallel, and distributed information processing during collective sensemaking.
Peter Kraft, Kaitlyn Zhou, Isabelle Edwards, Kate Starbird, and Emma S. Spiro
CHI'17. ACM Conference on Human Factors in Computing Systems. 2017