Ryan Liu
Hi! I am a second year PhD student at Princeton Computer Science advised by Tom Griffiths. The central focus of my research is how large language models can transform how our society communicates and learns information. Previously, I was a Masters student at Carnegie Mellon working with Nihar Shah on solving central problems in conference peer review.
I am happy to chat about my current research and future opportunities! Please contact me via email at ryanliu@princeton.edu.
Papers
- LLM Social Simulations Are a Promising Research Method
Jacy Reese Anthis, Ryan Liu, Sean M. Richardson, Austin C. Kozlowski, Bernard Koch, Erik Brynjolfsson, James Evans, Michael Bernstein
Link coming soon...
- On Benchmarking Human-like Intelligence
Lance Ying, Katherine M. Collins, Lionel Wong, Ilia Sucholutsky, Ryan Liu, Adrian Weller, Tianmin Shu, Thomas L. Griffiths, Joshua B. Tenenbaum
Preprint [arXiv]
- RLHS: Mitigating Misalignment in RLHF with Hindsight Simulation
Kaiqu Liang, Haimin Hu, Ryan Liu, Thomas L. Griffiths, Jaime Fernández Fisac
Preprint [arXiv]
- Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
Ryan Liu*, Jiayi Geng*, Addison J. Wu, Ilia Sucholutsky, Tania Lombrozo, and Thomas L. Griffiths
Preprint [arXiv]
- Large Language Models Assume People are More Rational than We Really are
Ryan Liu*, Jiayi Geng*, Joshua C. Peterson, Ilia Sucholutsky, and Thomas L. Griffiths
ICLR 2025 [arXiv]
- How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
Ryan Liu*, Theodore R. Sumers*, Ishita Dasgupta, and Thomas L. Griffiths
ICML 2024, Oral [arXiv]
- Improving Interpersonal Communication by Simulating Audiences with Language Models
Ryan Liu, Howard Yen, Raja Marjieh, Thomas L. Griffiths, and Ranjay Krishna
Preprint [arXiv]
- API-Assisted Code Generation for Question Answering on Varied Table Structures
Yihan Cao*, Shuyi Chen*, Ryan Liu*, Zhiruo Wang, and Daniel Fried
EMNLP 2023 [arXiv]
- ReviewerGPT? An Exploratory Study on Using Large Language Models for Paper Reviewing
Ryan Liu and Nihar B. Shah
Oral, AAAI SDU Workshop 2024 [arXiv]
- LLMs as Workers in Human-Computational Algorithms? Replicating Crowdsourcing Pipelines with LLMs
Tongshuang Wu, Haiyi Zhu, Maya Albayrak, Alexis Axon, Amanda Bertsch, Wenxing Deng, Ziqi Ding, Bill Guo, Sireesh Gururaja, Tzu-Sheng Kuo, Jenny T Liang, Ryan Liu, Ihita Mandal, Jeremiah Milbauer, Xiaolin Ni, Namrata Padmanabhan, Subhashini Ramkumar, Alexis Sudjianto, Jordan Taylor, Ying-Jui Tseng, Patricia Vaidos, Zhijin Wu, Wei Wu, Chenyang Yang
[arXiv]
- Testing for Reviewer Anchoring in Peer Review: A Randomized Controlled Trial
Ryan Liu, Steven Jecmen, Fei Fang, Vincent Conitzer, and Nihar B. Shah
PLoS ONE [arXiv]
- Cite-seeing and Reviewing: A Study on Citation Bias in Peer Review
Ivan Stelmakh, Charvi Rastogi, Ryan Liu, Shuchi Chawla, Federico Echenique, and Nihar B. Shah
PLoS ONE [arXiv]
- Near-Optimal Reviewer Splitting in Two-Phase Paper Reviewing & Conference Experiment Design
Steven Jecmen, Hanrui Zhang, Ryan Liu, Fei Fang, Vincent Conitzer, and Nihar B. Shah
AAAI HCOMP 2022, Best Paper Honorable Mention [arXiv]
- Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments
Steven Jecmen, Hanrui Zhang, Ryan Liu, Nihar B. Shah, Vincent Conitzer, and Fei Fang
NeurIPS 2020 [arXiv]
Presentations
- Talk @ The Stanford NLP Group Seminar
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
- Talk @ Stanford HCI Group
Predicting and Simulating New People using Existing Agents
- Talk @ UT-Austin Natural Language Learning Reading Group
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
- Talk @ Google DeepMind
Mind Your Step (by Step): Chain-of-Thought can Reduce Performance on Tasks where Thinking Makes Humans Worse
- Oral @ International Conference on Machine Learning 2024
How do Large Language Models Navigate Conflicts between Honesty and Helpfulness?
- Moderator @ London Machine Learning Meetup
Joon Sung Park | Generative Agents: Interactive Simulacra of Human Behavior [recording]
- Podcast @ Data Skeptic
Automated Peer Review [link]
- Visit @ Allen Institute for AI, Semantic Scholar Team
- Talk @ Carnegie Mellon University Meeting of the Minds 2022
Identifying Human Biases in Peer Review via Real-Subject Experiments
- Poster @ Carnegie Mellon University Meeting of the Minds 2021
Improving Algorithmic Tools for Conference Peer Review Research
- Poster @ Carnegie Mellon University Fall Undergraduate Research Showcase 2020
Creating Robustness within Conference Peer Review
- Poster @ Carnegie Mellon University Meeting of the Minds 2020
Assignment Algorithms to Prevent Quid-Pro-Quo in Conference Peer Review
Experience
- Assistant in Instruction @ Princeton | Advanced Topics in Computer Science: Machine Behavior
- Assistant in Instruction @ Princeton | Ethics of Computing
- AI/ML SWE Internship @ Meta
- Teaching Assistant @ CMU | 15-112 Fundamentals of Programming
- Research Assistant @ CMU School of Computer Science
Academic Honors
- Reviewer, ICLR 2025 Workshop on Bidirectional Human-AI Alignment
- Reviewer, ICLR 2025
- Reviewer, NeurIPS 2024 Workshop on Behavioral ML
- Reviewer, NeurIPS 2024
- Student Organizer, Decentralized Social Media Workshop @Princeton
- NSF Research Experience for Undergraduates Grant (CMU)
- Bachelor of Science, CMU School of Computer Science, College & University Honors
- Fifth-Year Master's, CMU School of Computer Science, Thesis: Testing for Reviewer Anchoring in the Conference Rebuttal Process [link]