TY - GEN
T1 - Towards Trustworthy Large Language Models
AU - Koyejo, Sanmi
AU - Li, Bo
N1 - National Science Foundation award number(s): 2046795, 2205329
PY - 2024/3/4
Y1 - 2024/3/4
N2 - Large Language models are among the most exciting technologies developed in the last few years. While the model's capabilities continue to improve, researchers, practitioners, and the general public are increasingly aware of some of its shortcomings. What will it take to build trustworthy large language models? This tutorial will present a range of recent findings, discussions, questions, and partial answers in the space of trustworthiness in large language models. While this tutorial will not attempt a comprehensive overview of this rich area, we aim to provide the participants with some tools and insights and to understand both the conceptual foundations of trustworthiness and a broad range of ongoing research efforts. We will tackle some of the hard questions that you may have about trustworthy large language models and hopefully address some misconceptions that have become pervasive.
AB - Large Language models are among the most exciting technologies developed in the last few years. While the model's capabilities continue to improve, researchers, practitioners, and the general public are increasingly aware of some of its shortcomings. What will it take to build trustworthy large language models? This tutorial will present a range of recent findings, discussions, questions, and partial answers in the space of trustworthiness in large language models. While this tutorial will not attempt a comprehensive overview of this rich area, we aim to provide the participants with some tools and insights and to understand both the conceptual foundations of trustworthiness and a broad range of ongoing research efforts. We will tackle some of the hard questions that you may have about trustworthy large language models and hopefully address some misconceptions that have become pervasive.
UR - http://www.scopus.com/inward/record.url?scp=85191760363&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85191760363&partnerID=8YFLogxK
U2 - 10.1145/3616855.3636454
DO - 10.1145/3616855.3636454
M3 - Conference contribution
AN - SCOPUS:85191760363
T3 - WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining
SP - 1126
EP - 1127
BT - WSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining
PB - Association for Computing Machinery
T2 - 17th ACM International Conference on Web Search and Data Mining, WSDM 2024
Y2 - 4 March 2024 through 8 March 2024
ER -