TrustLLM: Trustworthiness in Large Language Models Article Swipe

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Lichao Sun , Y. Huang , Haoran Wang , Siyuan Wu , Qihui Zhang , Chujie Gao , Yixing Huang , Wenhan Lyu , Yixuan Zhang , Xiner Li , Zhengliang Liu , Yixin Liu , Yijue Wang , Zhikun Zhang , Bhavya Kailkhura , Caiming Xiong , Chao Zhang , Chaowei Xiao , Chunyuan Li , Eric P. Xing , Furong Huang , Hao Liu , Heng Ji , Hongyi Wang , Huan Zhang , Huaxiu Yao , Manolis Kellis , Marinka Žitnik , Meng Jiang , Mohit Bansal , James Zou , Jian Pei , J. Liu , Jianfeng Gao , Jiawei Han , Jieyu Zhao , Ji‐Liang Tang , Jindong Wang , John C. Mitchell , Kai Shu , Kaidi Xu , Kai‐Wei Chang , Lifang He , Lifu Huang , Michael Backes , Neil Zhenqiang Gong , Philip S. Yu , Pin‐Yu Chen , Quanquan Gu , Ran Xu , Rex Ying , Shuiwang Ji , Suman Jana , Tianlong Chen , Tianming Liu , Tianyi Zhou , Willian Wang , Xiang Li , Xiangliang Zhang , Xiao Wang , Xing Xie , Xun Chen , Xuyu Wang , Yan Liu , Yanfang Ye , Yinzhi Cao , Yue Zhao ·

YOU? · · 2024 · Open Access · · DOI: https://doi.org/10.48550/arxiv.2401.05561

Large language models (LLMs), exemplified by ChatGPT, have gained considerable attention for their excellent natural language processing capabilities. Nonetheless, these LLMs present many challenges, particularly in the realm of trustworthiness. Therefore, ensuring the trustworthiness of LLMs emerges as an important topic. This paper introduces TrustLLM, a comprehensive study of trustworthiness in LLMs, including principles for different dimensions of trustworthiness, established benchmark, evaluation, and analysis of trustworthiness for mainstream LLMs, and discussion of open challenges and future directions. Specifically, we first propose a set of principles for trustworthy LLMs that span eight different dimensions. Based on these principles, we further establish a benchmark across six dimensions including truthfulness, safety, fairness, robustness, privacy, and machine ethics. We then present a study evaluating 16 mainstream LLMs in TrustLLM, consisting of over 30 datasets. Our findings firstly show that in general trustworthiness and utility (i.e., functional effectiveness) are positively related. Secondly, our observations reveal that proprietary LLMs generally outperform most open-source counterparts in terms of trustworthiness, raising concerns about the potential risks of widely accessible open-source LLMs. However, a few open-source LLMs come very close to proprietary ones. Thirdly, it is important to note that some LLMs may be overly calibrated towards exhibiting trustworthiness, to the extent that they compromise their utility by mistakenly treating benign prompts as harmful and consequently not responding. Finally, we emphasize the importance of ensuring transparency not only in the models themselves but also in the technologies that underpin trustworthiness. Knowing the specific trustworthy technologies that have been employed is crucial for analyzing their effectiveness.

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Computer Science

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Trustworthiness Mainstream Transparency (behavior) Computer science Political science Computer security Law

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Type: preprint
Language: en
Landing Page: http://arxiv.org/abs/2401.05561
PDF: https://arxiv.org/pdf/2401.05561
OA Status: green
Cited By: 47
Related Works: 10
OpenAlex ID: https://openalex.org/W4390833061

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OpenAlex ID: https://openalex.org/W4390833061

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DOI: https://doi.org/10.48550/arxiv.2401.05561

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Title: TrustLLM: Trustworthiness in Large Language Models

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Type: preprint

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Language: en

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Publication year: 2024

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Publication date: 2024-01-10

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Authors: Lichao Sun, Y. Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixing Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chao Zhang, Chaowei Xiao, Chunyuan Li, Eric P. Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Žitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, J. Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Ji‐Liang Tang, Jindong Wang, John C. Mitchell, Kai Shu, Kaidi Xu, Kai‐Wei Chang, Lifang He, Lifu Huang, Michael Backes, Neil Zhenqiang Gong, Philip S. Yu, Pin‐Yu Chen, Quanquan Gu, Ran Xu, Rex Ying, Shuiwang Ji, Suman Jana, Tianlong Chen, Tianming Liu, Tianyi Zhou, Willian Wang, Xiang Li, Xiangliang Zhang, Xiao Wang, Xing Xie, Xun Chen, Xuyu Wang, Yan Liu, Yanfang Ye, Yinzhi Cao, Yue Zhao

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Landing page: https://arxiv.org/abs/2401.05561

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PDF URL: https://arxiv.org/pdf/2401.05561

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Open access: Yes

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Concepts: Trustworthiness, Mainstream, Transparency (behavior), Computer science, Political science, Computer security, Law

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Cited by: 47

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