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Computer Science

D-Index
100
Citations
47777
World Ranking
365
National Ranking
199

Research.com Recognitions

  • 2015 - Fellow of Alfred P. Sloan Foundation

Overview

Percy Liang is affiliated with Stanford University in the United States. Their scholarly work is primarily centered in the field of Computer Science, with a significant focus on Artificial Intelligence. Other areas of research include Computer Vision and Pattern Recognition, Information Systems, Molecular Biology, and Safety Research.

The main research topics covered by Percy Liang encompass:

  • Topic Modeling
  • Natural Language Processing Techniques
  • Domain Adaptation and Few-Shot Learning
  • Multimodal Machine Learning Applications
  • Adversarial Robustness in Machine Learning
  • Machine Learning and Data Classification
  • Explainable Artificial Intelligence (XAI)

Some of the recent papers associated with Percy Liang's research include:

  • Emergent Abilities of Large Language Models, 2022, arXiv (Cornell University)
  • Lost in the Middle: How Language Models Use Long Contexts, 2024, Transactions of the Association for Computational Linguistics
  • Holistic Evaluation of Language Models, 2023, Annals of the New York Academy of Sciences
  • Prefix-Tuning: Optimizing Continuous Prompts for Generation, 2021, arXiv (Cornell University)
  • WILDS: A Benchmark of in-the-Wild Distribution Shifts, 2020, The Caltech Institute Archives (California Institute of Technology)

Frequent co-authors who have collaborated with Percy Liang include:

  • Rishi Bommasani
  • Tatsunori Hashimoto
  • Michihiro Yasunaga
  • Ananya Kumar
  • Kevin Klyman

Publications have appeared in venues such as:

  • arXiv (Cornell University)
  • SSRN Electronic Journal
  • Proceedings of the AAAI/ACM Conference on AI Ethics and Society
  • Transactions of the Association for Computational Linguistics
  • Annals of the New York Academy of Sciences

Percy Liang has been recognized as a Fellow of the Alfred P. Sloan Foundation, an award received in 2015.

Best Publications

  • SQuAD: 100,000+ Questions for Machine Comprehension of Text

    Pranav Rajpurkar;Jian Zhang;Konstantin Lopyrev;Percy Liang

  • Prefix-Tuning: Optimizing Continuous Prompts for Generation

    Xiang Lisa Li;Percy Liang

  • Know What You Don't Know: Unanswerable Questions for SQuAD

    Pranav Rajpurkar;Robin Jia;Percy Liang

  • On the Opportunities and Risks of Foundation Models.

    Rishi Bommasani;Drew A. Hudson;Ehsan Adeli;Russ Altman

  • Semantic Parsing on Freebase from Question-Answer Pairs

    Jonathan Berant;Andrew Chou;Roy Frostig;Percy Liang

  • Understanding black-box predictions via influence functions

    Pang Wei Koh;Percy Liang

  • Adversarial Examples for Evaluating Reading Comprehension Systems

    Robin Jia;Percy Liang

  • Generative Agents: Interactive Simulacra of Human Behavior

    Unknown

  • Holistic Evaluation of Language Models

    Unknown

  • QuAC: Question Answering in Context

    Eunsol Choi;He He;Mohit Iyyer;Mohit Iyyer;Mark Yatskar

  • Learning Dependency-Based Compositional Semantics

    Percy Liang;Michael Jordan;Dan Klein

  • Delete, retrieve, generate: A simple approach to sentiment and style transfer

    Juncen Li;Robin Jia;He He;Percy Liang

  • Data Recombination for Neural Semantic Parsing

    Robin Jia;Percy Liang

  • Compositional Semantic Parsing on Semi-Structured Tables

    Panupong Pasupat;Percy Liang

  • Semantic Parsing via Paraphrasing

    Jonathan Berant;Percy Liang

  • Strategies for Pre-training Graph Neural Networks

    Weihua Hu;Bowen Liu;Joseph Gomes;Marinka Zitnik

  • Certified Defenses against Adversarial Examples

    Aditi Raghunathan;Jacob Steinhardt;Percy Liang

  • Dropout Training as Adaptive Regularization

    Stefan Wager;Sida Wang;Percy S Liang

  • Diffusion-LM Improves Controllable Text Generation

    Unknown

  • Alignment by Agreement

    Percy Liang;Ben Taskar;Dan Klein

  • QA-GNN: Reasoning with Language Models and Knowledge Graphs for Question Answering.

    Michihiro Yasunaga;Hongyu Ren;Antoine Bosselut;Percy Liang

  • Distributionally Robust Neural Networks for Group Shifts: On the Importance of Regularization for Worst-Case Generalization.

    Shiori Sagawa;Pang Wei Koh;Tatsunori B. Hashimoto;Percy Liang

  • Unlabeled Data Improves Adversarial Robustness

    Yair Carmon;Aditi Raghunathan;Ludwig Schmidt;John C. Duchi

Frequent Co-Authors

Daniel Klein
Daniel Klein University of California, Berkeley
Christopher D. Manning
Christopher D. Manning Stanford University
Michael I. Jordan
Michael I. Jordan University of California, Berkeley
Jure Leskovec
Jure Leskovec Stanford University
John C. Duchi
John C. Duchi Stanford University
Tengyu Ma
Tengyu Ma Stanford University
Alex Aiken
Alex Aiken Stanford University
Sham M. Kakade
Sham M. Kakade Harvard University
Noah D. Goodman
Noah D. Goodman Stanford University

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