1 code implementation • 10 May 2024 • Ilia Kuznetsov, Osama Mohammed Afzal, Koen Dercksen, Nils Dycke, Alexander Goldberg, Tom Hope, Dirk Hovy, Jonathan K. Kummerfeld, Anne Lauscher, Kevin Leyton-Brown, Sheng Lu, Mausam, Margot Mieskes, Aurélie Névéol, Danish Pruthi, Lizhen Qu, Roy Schwartz, Noah A. Smith, Thamar Solorio, Jingyan Wang, Xiaodan Zhu, Anna Rogers, Nihar B. Shah, Iryna Gurevych
We hope that our work will help set the agenda for research in machine-assisted scientific quality control in the age of AI, within the NLP community and beyond.
1 code implementation • 13 Nov 2023 • Sheng Lu, Hendrik Schuff, Iryna Gurevych
In-context learning (ICL) has become one of the most popular learning paradigms.
1 code implementation • 18 Oct 2023 • Sheng Lu, Shan Chen, Yingya Li, Danielle Bitterman, Guergana Savova, Iryna Gurevych
In-context learning (ICL) is a new learning paradigm that has gained popularity along with the development of large language models.
1 code implementation • 4 Sep 2023 • Sheng Lu, Irina Bigoulaeva, Rachneet Sachdeva, Harish Tayyar Madabushi, Iryna Gurevych
Large language models, comprising billions of parameters and pre-trained on extensive web-scale corpora, have been claimed to acquire certain capabilities without having been specifically trained on them.
1 code implementation • 5 Apr 2023 • Shan Chen, Yingya Li, Sheng Lu, Hoang Van, Hugo JWL Aerts, Guergana K. Savova, Danielle S. Bitterman
The first task is classifying whether statements of clinical and policy recommendations in scientific literature constitute health advice.