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Chenglei Si

[email] [scholar] [twitter] [github]
1st Year PhD Student at Stanford NLP

About

At Stanford, I'm rotating with Diyi Yang, Michael Bernstein, and Tatsu Hashimoto. Before coming to Stanford, I did my undergrad at the University of Maryland where I was advised by Jordan Boyd-Graber.

Since my PhD, I have pivoted my research agenda to think about Machine Culture. As AI researchers, we have seen ample studies on how to adapt AI to align with human values, or complement human capabilities. But while humans are re-designing modern AI, the increasingly powerful and ubiquitous AI technologies (GPT-4, Midjourney, AlphaFold) are also producing real artifacts, impacting real people, and changing the way we humans think, communicate, and collaborate. At a macro level, the rise of generative AI is gradually shaping our value, culture, governance, and incentives as a society. I am intrigued to design empirical studies to rigorously understand such impact both at the individual level and at the societal level, and to envision the future of human-AI symbiosis.

When not writing papers, I also write research related Twitter threads for fun.

Recent Papers

  • Large Language Models Help Humans Verify Truthfulness -- Except When They Are Convincingly Wrong
    Chenglei Si, Navita Goyal, Sherry Tongshuang Wu, Chen Zhao, Shi Feng, Hal Daumé III, Jordan Boyd-Graber
    preprint [paper] [tweet]

  • Mixture of Prompt Experts for Generalizable and Interpretable Question Answering
    Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, Jordan Boyd-Graber
    EMNLP 2023 Findings [paper] [code]

  • Ignore This Title and HackAPrompt: Exposing Systemic Vulnerabilities of LLMs through a Global Scale Prompt Hacking Competition
    Sander Schulhoff*, Jeremy Pinto*, Anaum Khan, Louis-François Bouchard, Chenglei Si, Svetlina Anati, Valen Tagliabue, Anson Liu Kost, Christopher Carnahan, Jordan Boyd-Graber
    EMNLP 2023 [webpage]
    Best Theme Paper

  • Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
    Chenglei Si*, Dan Friedman*, Nitish Joshi, Shi Feng, Danqi Chen, He He
    ACL 2023 [paper] [code] [tweet] [OpenReview]

  • Prompting GPT-3 To Be Reliable
    Chenglei Si, Zhe Gan, Zhengyuan Yang, Shuohang Wang, Jianfeng Wang, Jordan Boyd-Graber, Lijuan Wang
    ICLR 2023 [paper] [code] [tweet] [video]

  • Sub-Character Tokenization for Chinese Pretrained Language Models
    Chenglei Si*, Zhengyan Zhang*, Yingfa Chen*, Fanchao Qi, Xiaozhi Wang, Zhiyuan Liu, Yasheng Wang, Qun Liu, Maosong Sun
    TACL 2023 [paper] [code]

  • Re-Examining Calibration: The Case of Question Answering
    Chenglei Si, Chen Zhao, Sewon Min, Jordan Boyd-Graber
    EMNLP 2022 Findings [paper] [code] [video]

Travels

  • Oct 2023, UIST @ San Francisco
  • July 2023, ACL @ Toronto
  • May 2023, ICLR @ Kigali
  • March 2023, Visit Day @ Stanford
  • March 2023, Visit Day @ NYU
  • Dec 2022, EMNLP @ Abu Dhabi
  • Summer 2022, Internship + NAACL @ Seattle