Nanyun Peng

About

Nanyun (Violet) Peng is an Associate Professor in the Department of Computer Science at UCLA Samueli School of Engineering. She holds a PhD in Computer Science from Johns Hopkins University, obtained in 2017. Her research focuses on Natural Language Processing and Machine Learning, with notable contributions in areas such as sarcasm generation with commonsense knowledge. Peng has received several awards, including the NSF CAREER Award in 2024, the Okawa Foundation Research Award in 2023, and the Google Research Scholar Award in 2023. Her work has been recognized through notable publications and keynote talks, and she is actively involved in advancing AI technologies related to language understanding and generation.

Research topics

• Artificial Intelligence
• Computer Science
• Social Science
• Psychology
• Engineering
• Data science
• Natural Language Processing
• Sociology
• Engineering ethics
• Management science

Selected publications

• 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

• An Empirical Study of Training End-to-End Vision-and-Language Transformers

  2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · 2022 · 313 citations

  Vision-and-language (VL) pre-training has proven to be highly effective on various VL downstream tasks. While recent work has shown that fully transformer-based VL models can be more efficient than previous region-feature-based methods, their performance on downstream tasks often degrades significantly. In this paper, we present Meter, a Multimodal End-to-end TransformER framework, through which we investigate how to design and pre-train a fully transformer-based VL model in an end-to-end manner…

  DOI

• Content Planning for Neural Story Generation with Aristotelian Rescoring

  2020 · 106 citations

  Senior authorCorresponding

  Long-form narrative text generated from large language models manages a fluent impersonation of human writing, but only at the local sentence level, and lacks structure or global cohesion. We posit that many of the problems of story generation can be addressed via highquality content planning, and present a system that focuses on how to learn good plot structures to guide story generation. We utilize a plot-generation language model along with an ensemble of rescoring models that each implement…

  DOI

• Next Steps for Human-Centered Generative AI: A Technical Perspective

  arXiv (Cornell University) · 2023 · 14 citations

  Through iterative, cross-disciplinary discussions, we define and propose next-steps for Human-centered Generative AI (HGAI). We contribute a comprehensive research agenda that lays out future directions of Generative AI spanning three levels: aligning with human values; assimilating human intents; and augmenting human abilities. By identifying these next-steps, we intend to draw interdisciplinary research teams to pursue a coherent set of emergent ideas in HGAI, focusing on their interested topi…

  PublisherOA PDFDOI

• A large-scale randomized study of large language model feedback in peer review

  Nature Machine Intelligence · 2026-02-23 · 6 citations

  article
  PublisherDOI

Recent grants

• Evidence Extraction Systems for the Molecular Interaction Literature

  NIH · $1.1M · 2017–2022

Frequent coauthors

• Kai-Wei Chang

  107 shared

• I-Hung Hsu

  52 shared

• Rujun Han

  44 shared

• Prem Natarajan

  42 shared

• Xuezhe Ma

  40 shared

• Aram Galstyan

  32 shared

• Tuhin Chakrabarty

  29 shared

• Kuan-Hao Huang

  28 shared

Education

• PhD., Center of Language and Signal Processing

  Johns Hopkins University

  2017

• M.S., computer science

  Peking University

  2012

• B.A., Center of Chinese Economic Research

  Peking University

  2009

• B.A., Chinese Linguistics and Literature

  Peking University

  2009

Awards & honors

• NSF CAREER Award (2024)
• Okawa Foundation Research Award (2023)
• Google Research Scholar Award (2023)
• NAACL Paper Award (2022)
• Keynote Talk at EMNLP (2019)

Similar researchers at University of California, Los Angeles

• Demetri Terzopoulosh-93
• Wei Wangh-91
• Eleazar Eskinh-83