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Towards Trustworthy Large Language Models

Published: 04 March 2024 Publication History

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.

References

[1]
Rylan Schaeffer, Brando Miranda, and Sanmi Koyejo. 2023. Are Emergent Abilities of Large Language Models a Mirage?. In Thirty-seventh Conference on Neural Information Processing Systems. https://openreview.net/forum?id=ITw9edRDlD
[2]
Boxin Wang, Weixin Chen, Hengzhi Pei, Chulin Xie, Mintong Kang, Chenhui Zhang, Chejian Xu, Zidi Xiong, Ritik Dutta, Rylan Schaeffer, Sang T. Truong, Simran Arora, Mantas Mazeika, Dan Hendrycks, Zinan Lin, Yu Cheng, Sanmi Koyejo, Dawn Song, and Bo Li. 2023. DecodingTrust: A Comprehensive Assessment of Trustworthiness in GPT Models. In Thirty-seventh Conference on Neural Information Processing Systems Datasets and Benchmarks Track. https://openreview.net/forum?id=kaHpo8OZw2
[3]
Chejian Xu, Ding Zhao, Alberto Sangiovanni-Vincentelli, and Bo Li. 2023. Diff-Scene: Diffusion-Based Safety-Critical Scenario Generation for Autonomous Vehicles. In The Second Workshop on New Frontiers in Adversarial Machine Learning. https://openreview.net/forum?id=hclEbdHida

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cover image ACM Conferences
WSDM '24: Proceedings of the 17th ACM International Conference on Web Search and Data Mining
March 2024
1246 pages
ISBN:9798400703713
DOI:10.1145/3616855
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Association for Computing Machinery

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Published: 04 March 2024

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