Fan Yin (银帆)

Hi, I am a fourth-year PhD student in the Computer Science department at University of California, Los Angeles (UCLA), advised by Prof.Kai-Wei Chang. Preivously, I received my B.S. degree in Computer Science from Peking University in 2020, where I have worked with Prof. Xiaojun Wan. My research interests are reliability, robustness, and interpretation for NLP systems. My recent research focuses on helping build more human trust and understanding on NLP model, through estimating and calibrating model predictions.

Email  /  linkedin  /  Github  /  CV  /  Google Scholar

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Education

Ph.D.          Sep. 2020 - Present
                       University of California Los Angeles (UCLA), Los Angeles, CA, U.S.
                       Ph.D. student in Computer Science

B.S.              Sep. 2016 - June 2020
                       Peking University (PKU), Beijing, China.
                       B.S. in Computer Science
Intern Experience

  • Jun 2023 -- Sep 2023, Amazon AWS, Santa Clara
    Applied Scientist Intern. Mentors: He He and Samson Tan. Manager: Aditya Rawal.

  • Jun 2022 -- Sep 2022, Salesforce Research, Palo Alto
    Research Intern. Mentors: Jesse Vig and Philippe Laban. Manager: Jason Wu.

  • Jun 2021 -- Sep 2021, Amazon AWS, remote
    Applied Scientist Intern. Mentor: Prof. He He

  • Jan.2019 -- Jun.2019, Sep.2019 -- Dec.2019, ShannonAI , Beijing, China
    Software Engineer Intern, advised by Jiwei Li

  • Jun.2019 -- Sep.2019, University of California, Los Angeles (UCLA) , CA, US
    Reseach Intern, advised by Prof. Kai-Wei Chang

Publications
Characterizing Truthfulness in Large Language Model Generations with Local Intrinsic Dimension
Fan Yin, Jayanth Srinivasa, Kai-Wei Chang
ICML 2024
[ paper | code ]
Prompt-Driven LLM Safeguarding via Directed Representation Optimization
Chujie Zheng, Fan Yin, Hao Zhou, Fandong Meng, Jie Zhou, Kai-Wei Chang, Minlie Huang, Nanyun Peng
ICML 2024
[ paper | code ]
Red Teaming Language Model Detectors with Language Models
Zhouxing Shi*, Yihan Wang*, Fan Yin*, Xiangning Chen, Kai-Wei Chang, Cho-Jui Hsieh
TACL, equal contribution, ordered alphabetically
[ paper | code ]
Active Instruction Tuning: Improving Cross-Task Generalization by Training on Prompt Sensitive Tasks
Po-Nien Kung, Fan Yin, Di Wu, Kai-Wei Chang, Nanyun Peng
EMNLP 2023
[ paper | code ]
Dynosaur: A Dynamic Growth Paradigm for Instruction-Tuning Data Curation
Da Yin*, Xiao Liu*, Fan Yin*, Ming Zhong*, Hritik Bansal, Jiawei Han, Kai-Wei Chang
EMNLP 2023, equal contribution
[ paper | code ]
Did You Read the Instructions? Rethinking the Effectiveness of Task Definitions in Instruction Learning
Fan Yin, Jesse Vig, Philippe Laban, Shafiq Joty, Caiming Xiong, Chien-Sheng Jason Wu
ACL 2023
[ paper | code ]
Efficient Shapley Values Estimation by Amortization for Text Classification
Chenghao Yang, Fan Yin, He He, Kai-Wei Chang, Xiaofei Ma, Bing Xiang
ACL 2023
[ paper | code ]
CleanCLIP: Mitigating Data Poisoning Attacks in Multimodal Contrastive Learning
Hritik Bansal, Nishad Singhi, Yu Yang, Fan Yin, Aditya Grover, Kai-Wei Chang
ICCV 2023, Oral | Best Paper Award at Trustworthy and Reliable Large-Scale ML@ICLR 2023
[ paper | code ]
ADDMU: Detection of Far-Boundary Adversarial Examples with Data and Model Uncertainty Estimation
Fan Yin, Yao Li, Cho-Jui Hsieh, Kai-Wei Chang
EMNLP 2022
[ paper | code ]
On the Sensitivity and Stability of Model Interpretations in NLP
Fan Yin, Zhouxing Shi, Cho-Jui Hsieh, Kai-Wei Chang
ACL 2022
[ paper | code ]
On the Robustness of Language Encoders against Grammatical Errors
Fan Yin, Quanyu Long, Tao Meng, Kai-Wei Chang
ACL 2020
[ paper | code ]
Entity-relation Extraction as Multi-turn Question Answering
Xiaoya Li*, Fan Yin*, Zijun Sun, Xiayu Li, Arianna Yuan, Duo Chai, Mingxin Zhou, Jiwei Li
ACL 2019, equal contribution
[ paper | code ]
Glyce: Glyph-vectors for Chinese Character Representations
Yuxian Meng, Wei Wu, Fei Wang, Xiaoya Li, Ping Nie, Fan Yin, Muyu Li, Qinghong Han, Xiaofei Sun, Jiwei Li
NeurIPS 2019
[ paper | code ]
Academic Services

  • Journal/Conference Reviewer: NeurIPS 2022, ACL 2022, NAACL 2022, NAACL Student Research Workshop 2022

Teaching

  • Teaching Associate position, UCLA CS M146, Introduction to Machine learning, Fall 2022, with Prof. Kai-Wei Chang

  • Teaching Assistant, UCLA CS M146, Introduction to Machine learning, Fall 2021, with Prof. Kai-Wei Chang

  • Teaching Assistant, UCLA CS M146, Introduction to Machine learning, Winter 2022, with Prof. Sriram Sankararaman

  • Teaching Assistant, UCLA CS M146, Introduction to Machine learning, Spring 2022, with Prof. Aditya Grover

Awards

  • Excellent Graduate, Peking University, 2020.

  • Merit Student, Peking University, 2019.


Credit to Zijie Huang