Towards Trustworthy Large Language Models

Sanmi Koyejo, Bo Li

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

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.

Original languageEnglish (US)
Title of host publicationWSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining
PublisherAssociation for Computing Machinery
Pages1126-1127
Number of pages2
ISBN (Electronic)9798400703713
DOIs
StatePublished - Mar 4 2024
Externally publishedYes
Event17th ACM International Conference on Web Search and Data Mining, WSDM 2024 - Merida, Mexico
Duration: Mar 4 2024Mar 8 2024

Publication series

NameWSDM 2024 - Proceedings of the 17th ACM International Conference on Web Search and Data Mining

Conference

Conference17th ACM International Conference on Web Search and Data Mining, WSDM 2024
Country/TerritoryMexico
CityMerida
Period3/4/243/8/24

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Computer Science Applications
  • Software

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