1st Year PhD Student at Stanford NLP
At Stanford, I'm rotating with Diyi Yang
and Michael Bernstein
. Before coming to Stanford, I did my undergrad at the University of Maryland where I was advised by Jordan Boyd-Graber
, while also working closely with
Hal Daumé III
, He He
, Danqi Chen
and Sherry Wu
In summer 2022, I did a research internship at Microsoft hosted by Zhe Gan
. Before that, I got into NLP research by working with
and Zhiyuan Liu
Nowadays, I'm fascinated by Human-Muppet Interaction
And I'm particularly concerned about the following questions:
How can humans verify what Muppets said, especially in tasks where humans lack the domain expertise?
How can we enable human-Muppet collaboration to complete tasks that humans or Muppets cannot solve alone?
What is the long-term impact of humans relying on Muppets?
How can we measure and improve the safety of Muppets?
In pursuing these research questions, I tend to move away from existing benchmarks and put human needs at the center
of my research.
I also aim to craft an interdisciplinary research agenda that connects insights from HCI, NLP, ML, Psychology, and Linguistics.
Back in the old days, I also worked on Question Answering, Tokenization, and Prompting.
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
Mixture of Prompt Experts for Generalizable and Interpretable Question Answering
Chenglei Si, Weijia Shi, Chen Zhao, Luke Zettlemoyer, Jordan Boyd-Graber
EMNLP 2023 Findings
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
Measuring Inductive Biases of In-Context Learning with Underspecified Demonstrations
Chenglei Si*, Dan Friedman*, Nitish Joshi, Shi Feng, Danqi Chen, He He
Prompting GPT-3 To Be Reliable
Sub-Character Tokenization for Chinese Pretrained Language Models
Re-Examining Calibration: The Case of Question Answering
EMNLP 2022 Findings
- 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