Ellie Pavlick

About

Ellie Pavlick is an Assistant Professor of Computer Science and Linguistics at Brown University and a Research Scientist at Google Deepmind. She leads the Language Understanding and Representation (LUNAR) Lab, which aims to understand how language functions and to develop computational models capable of understanding language in a manner similar to humans. Her lab's research projects broadly focus on language and often extend to studying capacities beyond language, such as conceptual representations, reasoning, learning, and generalization. The lab investigates how humans achieve these cognitive abilities, how computational models—particularly large language models and other "black box" AI systems—accomplish them, and what insights can be gained by comparing human and machine approaches. Ellie Pavlick's research frequently involves collaboration with experts outside of computer science, including those in cognitive science, neuroscience, and philosophy.

Research topics

• Computer Science
• Artificial Intelligence
• Natural Language Processing
• Psychology
• Data science
• Cognitive psychology
• World Wide Web
• Operating system
• Programming language
• Physics

Selected publications

• Helping Cancer Patients to Choose the Best Treatment: Towards Automated Data-Driven and Personalized Information Presentation of Cancer Treatment Options

  arXiv (Cornell University) · 2024 · 675 citations

  When a person is diagnosed with cancer, difficult decisions about treatments need to be made. In this chapter, we describe an interdisciplinary research project which aims to automatically generate personalized descriptions of treatment options for patients. We relied on two large databases provided by the Netherlands Comprehensive Cancer Organisation (IKNL): The Netherlands Cancer Registry and the PROFILES dataset. Combining these datasets allowed us to extract personalized information about tr…

  DOI

• Beyond the Imitation Game: Quantifying and extrapolating the capabilities of language models

  arXiv (Cornell University) · 2022 · 548 citations

  Language models demonstrate both quantitative improvement and new qualitative capabilities with increasing scale. Despite their potentially transformative impact, these new capabilities are as yet poorly characterized. In order to inform future research, prepare for disruptive new model capabilities, and ameliorate socially harmful effects, it is vital that we understand the present and near-future capabilities and limitations of language models. To address this challenge, we introduce the Beyon…

  DOI

• Which Linguist Invented the Lightbulb? Presupposition Verification for Question-Answering

  2021 · 27 citations

  Najoung Kim, Ellie Pavlick, Burcu Karagol Ayan, Deepak Ramachandran. Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). 2021.

  DOI

• LLMs as models for analogical reasoning

  Journal of Memory and Language · 2025-07-31 · 6 citations

  articleOpen accessSenior author

  Analogical reasoning — the capacity to identify and map structural relationships between different domains — is fundamental to human cognition and learning. Recent studies have shown that large language models (LLMs) can sometimes match humans in analogical reasoning tasks, opening the possibility that analogical reasoning might emerge from domain-general processes. However, it is still debated whether these emergent capacities are largely superficial and limited to simple relations seen during…

  PublisherDOI

• Parallel trade-offs in human cognition and neural networks: The dynamic interplay between in-context and in-weight learning

  Proceedings of the National Academy of Sciences · 2025-08-28 · 6 citations

  articleOpen accessCorresponding

  Human learning embodies a striking duality: Sometimes, we can rapidly infer and compose logical rules, benefiting from structured curricula (e.g., in formal education), while other times, we rely on an incremental approach or trial-and-error, learning better from curricula that are randomly interleaved. Influential psychological theories explain this seemingly conflicting behavioral evidence by positing two qualitatively different learning systems-one for rapid, rule-based inferences (e.g., in w…

  PublisherOA PDFDOI

Frequent coauthors

• Roma Patel

  52 shared

• Ian Tenney

  49 shared

• Benjamin Van Durme

  43 shared

• Najoung Kim

  43 shared

• Tal Linzen

  36 shared

• Samuel R. Bowman

  34 shared

• Adam Poliak

  33 shared

• Patrick Xia

  33 shared

Similar researchers at Brown University

• Michael L. Littmanh-83
• Maurice P Herlihyh-72
• Roberto Tamassiah-69