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TrustLLM: Trustworthiness in Large Language Models Academic Paper Alert!

Written by Lichao Sun, Yue Huang, Haoran Wang, Siyuan Wu, Qihui Zhang, Chujie Gao, Yixin Huang, Wenhan Lyu, Yixuan Zhang, Xiner Li, Zhengliang Liu, Yixin Liu, Yijue Wang, Zhikun Zhang, Bhavya Kailkhura, Caiming Xiong, Chaowei Xiao, Chunyuan Li, Eric Xing, Furong Huang, Hao Liu, Heng Ji, Hongyi Wang, Huan Zhang, Huaxiu Yao, Manolis Kellis, Marinka Zitnik, Meng Jiang, Mohit Bansal, James Zou, Jian Pei, Jian Liu, Jianfeng Gao, Jiawei Han, Jieyu Zhao, Jiliang Tang, Jindong Wang, John 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, Yong Chen, Yue Zhao

Category: AI for IT

Article Section: Ethical and Responsible AI; AI Governance Frameworks

Publication Date: 2024-01-13

SEO Description: Exploring TrustLLM’s approach to enhancing trust in large language models through a comprehensive study and benchmarking.

Sun, Lichao, et al. TrustLLM: Trustworthiness in Large Language Models. arXiv:2401.05561, arXiv, 13 Jan. 2024,


trustworthiness, large language models, principles, benchmark, evaluation

A digital heart with circuitry and electrical impulses on a motherboard background, symbolizing the fusion of technology and healthcare.
A digital heart with circuitry and electrical impulses on a motherboard background, symbolizing the fusion of technology and healthcare.

AI-Generated Paper Summary

Generated by Ethical AI Researcher GPT

The paper “TRUSTLLM: Trustworthiness in Large Language Models” focuses on the study of trustworthiness in Large Language Models (LLMs) like ChatGPT. It presents a comprehensive framework called TRUSTLLM, which includes principles for different dimensions of trustworthiness, benchmark evaluations, and analyses of mainstream LLMs. The study also discusses open challenges and future directions in this field​​.


  • Introduction and Background: The paper discusses the significance of LLMs in various applications and highlights their exceptional capabilities in natural language processing (NLP). However, it also points out the concerns regarding their trustworthiness, stemming from the complexity of outputs, potential biases in training data, and high user expectations​​.
  • Efforts by Developers: Various strategies employed by developers like OpenAI and Meta to enhance LLMs’ trustworthiness are mentioned. These include measures in training data phase, training methods, downstream applications, and alignment with ethical considerations​​.
  • Benchmarking Trustworthiness: The paper emphasizes the challenges in defining comprehensive aspects of trustworthiness, scalability, generalizability, and practical evaluation methodologies. It also acknowledges the need for a more nuanced approach to assess LLMs’ trustworthiness​​.
  • TRUSTLLM Framework: This includes identifying eight facets of trustworthiness, selecting a diverse range of LLMs for evaluation, and benchmarking across various tasks and datasets. The framework is designed to assess LLMs across multiple dimensions of trustworthiness​​.

Author Caliber:

  • The paper involves authors from numerous prestigious institutions, highlighting a high caliber of expertise and credibility.

Novelty & Merit:

  1. Comprehensive study of trustworthiness in LLMs.
  2. Development of a new framework, TRUSTLLM.
  3. Evaluation of LLMs across multiple dimensions of trustworthiness.
  4. Inclusion of a diverse range of LLMs and datasets in the study.

Findings and Conclusions:

  1. Positive correlation between trustworthiness and utility in LLMs.
  2. Proprietary LLMs generally outperform open-source models in terms of trustworthiness.
  3. Challenges in balancing safety without over-caution in LLMs.
  4. Variability in performance in open-ended tasks and out-of-distribution tasks.
  5. Need for continued research efforts to enhance reliability and ethical alignment of LLMs​​.

Commercial Applications:

  1. Improvement of LLMs in various applications, including automated article writing, translation, and software engineering.
  2. Application of LLMs in financial domain for tasks like sentiment analysis and news classification.
  3. Use in scientific research, spanning multiple domains including healthcare, political science, and law​​.

Overall, the paper provides valuable insights into the trustworthiness of LLMs and proposes a framework to evaluate and enhance their ethical alignment and reliability.

Author’s Abstract

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.

Read the full paper here

Last updated on January 20th, 2024.