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

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.