no code implementations • 1 Apr 2024 • Weixin Liang, Yaohui Zhang, Zhengxuan Wu, Haley Lepp, Wenlong Ji, Xuandong Zhao, Hancheng Cao, Sheng Liu, Siyu He, Zhi Huang, Diyi Yang, Christopher Potts, Christopher D Manning, James Y. Zou
To address this gap, we conduct the first systematic, large-scale analysis across 950, 965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time.
no code implementations • 11 Mar 2024 • Weixin Liang, Zachary Izzo, Yaohui Zhang, Haley Lepp, Hancheng Cao, Xuandong Zhao, Lingjiao Chen, Haotian Ye, Sheng Liu, Zhi Huang, Daniel A. McFarland, James Y. Zou
We present an approach for estimating the fraction of text in a large corpus which is likely to be substantially modified or produced by a large language model (LLM).
no code implementations • 15 Sep 2020 • Haley Lepp, Gina-Anne Levow
This study presents a corpus of turn changes between speakers in U. S. Supreme Court oral arguments.
no code implementations • WS 2019 • Haley Lepp
The United States Supreme Court plays a key role in defining the legal basis for gender discrimination throughout the country, yet there are few checks on gender bias within the court itself.
no code implementations • NAACL 2019 • Haley Lepp, Olga Zamaraeva, Emily M. Bender
We present a web-based system that facilitates the exploration of complex morphological patterns found in morphologically very rich languages.