Christopher Potts
Stanford University · Symbolic Systems
Active 2001–2026
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About
Christopher Potts is a Professor of Linguistics at Stanford University. He holds a B.A. in Linguistics with a German minor from New York University, obtained in 1999, and an M.A. and Ph.D. in Linguistics from the University of California, Santa Cruz, completed in 2000 and 2003 respectively. His academic focus includes applied logic, artificial intelligence, cognitive science, natural language, and philosophical foundations. In addition to his role in the Department of Linguistics, he is a member of the Bio-X Faculty and an affiliate of the Institute for Human-Centered Artificial Intelligence (HAI).
Research topics
- Computer Science
- Artificial Intelligence
- Political Science
- Data science
- Management science
- Engineering
- Psychology
- Engineering ethics
- Law
Selected publications
On the Opportunities and Risks of Foundation Models
arXiv (Cornell University) · 2021 · 2169 citations
AI is undergoing a paradigm shift with the rise of models (e.g., BERT, DALL-E, GPT-3) that are trained on broad data at scale and are adaptable to a wide range of downstream tasks. We call these models foundation models to underscore their critically central yet incomplete character. This report provides a thorough account of the opportunities and risks of foundation models, ranging from their capabilities (e.g., language, vision, robotics, reasoning, human interaction) and technical principles(…
Quantifying large language model usage in scientific papers
Nature Human Behaviour · 2025-08-04 · 30 citations
articleWARP: An Efficient Engine for Multi-Vector Retrieval
2025-07-13 · 5 citations
articleOpen accessMulti-vector retrieval methods such as ColBERT and its recent variant, the ConteXtualized Token Retriever (XTR), offer high accuracy but face efficiency challenges at scale. To address this, we present WARP, a retrieval engine that substantially improves the efficiency of retrievers trained with the XTR objective through three key innovations: (1) WARPSELECT for dynamic similarity imputation; (2) implicit decompression, avoiding costly vector reconstruction during retrieval; and (3) a two-stage…
Do Language Models Use Their Depth Efficiently?
ArXiv.org · 2025-05-20 · 1 citations
preprintOpen accessSenior authorModern LLMs are increasingly deep, and depth correlates with performance, albeit with diminishing returns. However, do these models use their depth efficiently? Do they compose more features to create higher-order computations that are impossible in shallow models, or do they merely spread the same kinds of computation out over more layers? To address these questions, we analyze the residual stream of the Llama 3.1, Qwen 3, and OLMo 2 family of models. We find: First, comparing the output of the…
Gastrointestinal Endoscopy · 2026-05-01
article
Recent grants
Expressive Content and the Semantics of Contexts
NSF · $218k · 2007–2012
RI: Medium: Bringing Sentiment Analysis and Social Network Analysis Together
NSF · $1.0M · 2012–2017
Frequent coauthors
- 50 shared
Atticus Geiger
- 45 shared
Zhengxuan Wu
- 44 shared
Christopher D. Manning
- 33 shared
Omar Khattab
Stanford University
- 33 shared
Elisa Kreiss
- 27 shared
Noah D. Goodman
- 25 shared
Matei Zaharia
- 19 shared
Samuel R. Bowman
Awards & honors
- Stanford Honors Thesis Prizes - Symbolic Systems
- Glushko Prize for Excellence in Undergraduate Research in Sy…
- Barwise Award for Distinguished Contributions to Symbolic Sy…
- Symbolic Systems Distinguished Teaching Award
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