OpenScholar: Synthesizing Scientific Literature with Retrieval-augmented LMs

Scientific progress depends on researchers' ability to synthesize the growing body of literature. Can large language models (LMs) assist scientists in this task? We introduce OpenScholar, a specialized retrieval-augmented LM that answers scientific queries by identifying relevant passages from...

Ausführliche Beschreibung

Bibliographische Detailangaben

Hauptverfasser:	Asai, Akari, He, Jacqueline, Shao, Rulin, Shi, Weijia, Singh, Amanpreet, Chang, Joseph Chee, Lo, Kyle, Soldaini, Luca, Feldman, Sergey, D'arcy, Mike, Wadden, David, Latzke, Matt, Tian, Minyang, Ji, Pan, Liu, Shengyan, Tong, Hao, Wu, Bohao, Xiong, Yanyu, Zettlemoyer, Luke, Neubig, Graham, Weld, Dan, Downey, Doug, Yih, Wen-tau, Koh, Pang Wei, Hajishirzi, Hannaneh
Format:	Artikel
Sprache:	eng
Schlagworte:	Computer Science - Artificial Intelligence Computer Science - Computation and Language Computer Science - Digital Libraries Computer Science - Information Retrieval Computer Science - Learning
Online-Zugang:	Volltext bestellen
Tags:	Tag hinzufügen Keine Tags, Fügen Sie den ersten Tag hinzu!