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Yuhui Zhang
Researcher at Tsinghua University
Publications - 17
Citations - 1568
Yuhui Zhang is an academic researcher from Tsinghua University. The author has contributed to research in topics: Computer science & Tokenization (data security). The author has an hindex of 7, co-authored 11 publications receiving 583 citations. Previous affiliations of Yuhui Zhang include Stanford University.
Papers
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Proceedings ArticleDOI
Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
TL;DR: This work introduces Stanza, an open-source Python natural language processing toolkit supporting 66 human languages that features a language-agnostic fully neural pipeline for text analysis, including tokenization, multi-word token expansion, lemmatization, part-of-speech and morphological feature tagging, dependency parsing, and named entity recognition.
Journal ArticleDOI
Holistic Evaluation of Language Models
Percy Liang,Rishi Bommasani,Tony Lee,Dimitris Tsipras,Dilara Soylu,Michihiro Yasunaga,Yian Zhang,Deepak Narayanan,Yuhuai Wu,Ananya Kumar,Benjamin Newman,Binhang Yuan,Bobby Yan,Ce Zhang,Christian Cosgrove,Christopher D. Manning,Christopher R'e,Diana Acosta-Navas,Drew A. Hudson,Eric Zelikman,Esin Durmus,Faisal Ladhak,Frieda Rong,Hongyu Ren,Huaxiu Yao,Jue Wang,Keshav Santhanam,Laurel Orr,Lucia Zheng,Byron Rogers,Mirac M. Suzgun,Nathan S. Kim,Neel Guha,Niladri S. Chatterji,Peter Henderson,Qian Huang,Ryan Chi,Michael Xie,Shibani Santurkar,Surya Ganguli,Tatsunori Hashimoto,Thomas Icard,Tianyi Zhang,Vishrav Chaudhary,William Wang,Xuechen Li,Yifan Mai,Yuhui Zhang,Yuta Koreeda +48 more
TL;DR: The Holistic Evaluation of Language Models (HELM) as mentioned in this paper ) is a popular benchmark for language models, with 30 models evaluated on 16 core scenarios and 7 metrics, exposing important trade-offs.
Posted Content
On the Opportunities and Risks of Foundation Models.
Rishi Bommasani,Drew A. Hudson,Ehsan Adeli,Russ B. Altman,Simran Arora,Sydney von Arx,Michael S. Bernstein,Jeannette Bohg,Antoine Bosselut,Emma Brunskill,Erik Brynjolfsson,Shyamal Buch,Dallas Card,Rodrigo Castellon,Niladri S. Chatterji,Annie Chen,Kathleen Creel,Jared Davis,Dora Demszky,Chris Donahue,Moussa Doumbouya,Esin Durmus,Stefano Ermon,John Etchemendy,Kawin Ethayarajh,Li Fei-Fei,Chelsea Finn,Trevor Gale,Lauren Gillespie,Karan Goel,Noah D. Goodman,Shelby Grossman,Neel Guha,Tatsunori Hashimoto,Peter Henderson,John Hewitt,Daniel E. Ho,Jenny Hong,Kyle Hsu,Jing Huang,Thomas Icard,Saahil Jain,Dan Jurafsky,Pratyusha Kalluri,Siddharth Karamcheti,Geoff Keeling,Fereshte Khani,Omar Khattab,Pang Wei Koh,Mark Krass,Ranjay Krishna,Rohith Kuditipudi,Ananya Kumar,Faisal Ladhak,Mina Lee,Tony Lee,Jure Leskovec,Isabelle Levent,Xiang Lisa Li,Xuechen Li,Tengyu Ma,Ali Ahmad Malik,Christopher D. Manning,Suvir Mirchandani,Eric Mitchell,Zanele Munyikwa,Suraj Nair,Avanika Narayan,Deepak Narayanan,Ben Newman,Allen Nie,Juan Carlos Niebles,Hamed Nilforoshan,Julian Nyarko,Giray Ogut,Laurel Orr,Isabel Papadimitriou,Joon Sung Park,Chris Piech,Eva Portelance,Christopher Potts,Aditi Raghunathan,Rob Reich,Hongyu Ren,Frieda Rong,Yusuf H. Roohani,Camilo Ruiz,Jack Ryan,Christopher Ré,Dorsa Sadigh,Shiori Sagawa,Keshav Santhanam,Andy Shih,Krishnan Srinivasan,Alex Tamkin,Rohan Taori,Armin W. Thomas,Florian Tramèr,Rose E. Wang,William Yang Wang,Bohan Wu,Jiajun Wu,Yuhuai Wu,Sang Michael Xie,Michihiro Yasunaga,Jiaxuan You,Matei Zaharia,Michael Zhang,Tianyi Zhang,Xikun Zhang,Yuhui Zhang,Lucia Zheng,Kaitlyn Zhou,Percy Liang +113 more
TL;DR: The authors 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(e. g.g. model architectures, training procedures, data, systems, security, evaluation, theory) to their applications.
Posted Content
Biomedical and Clinical English Model Packages in the Stanza Python NLP Library
TL;DR: The study introduces biomedical and clinical NLP packages built for the Stanza library, which offer performance that is similar to the state of the art, and is also optimized for ease of use.
Proceedings ArticleDOI
Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning
TL;DR: Modality gap, an intriguing geometric phenomenon of the representation space of multi-modal models, is presented and it is demonstrated that varying the modality gap distance has a significant impact in improving the model’s downstream zero-shot classi-cation performance and fairness.