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Chenglei Si

[email] [scholar] [twitter] [github]
2nd Year PhD at Stanford NLP

About

At Stanford, I'm advised by the wonderful Tatsu Hashimoto and Diyi Yang. Before coming to Stanford, I did my undergrad at the University of Maryland where I was advised by Jordan Boyd-Graber. I also had the fortune to work with many amazing research mentors and collaborators, including Michael Bernstein, Hal Daumé III, He He, Danqi Chen, Sherry Wu, and many others.

I'm interested in how LLMs can transform scientific research, and new forms of human-AI co-intelligence. Towards this end, I split my time between working with LLMs and running human studies. When not writing papers, I also write research related Twitter threads for fun.

Selected Papers

• Can LLMs Generate Novel Research Ideas? A Large-Scale Human Study with 100+ NLP Researchers
  Chenglei Si, Diyi Yang, Tatsunori Hashimoto
  preprint [paper] [code] [tweet] [Nature News] [Newsweek] [Synced]
  


• Design2Code: How Far Are We From Automating Front-End Engineering?
  Chenglei Si*, Yanzhe Zhang*, Zhengyuan Yang, Ruibo Liu, Diyi Yang
  preprint [paper] [code] [project page] [tweet] [WorldofAI]
  


• 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
  NAACL 2024 [paper] [tweet]
  


• 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]
  

Conference Travels

• June 2024, NAACL @ Mexico City
• Oct 2023, UIST @ San Francisco
• July 2023, ACL @ Toronto
• May 2023, ICLR @ Kigali
• Dec 2022, EMNLP @ Abu Dhabi
• July 2022, NAACL @ Seattle