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.

翻译：大型语言模型（LLMs），以ChatGPT为代表，因其卓越的自然语言处理能力而备受关注。然而，这些LLMs也带来诸多挑战，尤其在可信度方面。因此，确保LLMs的可信度成为重要议题。本文介绍TrustLLM——一项针对LLMs可信度的综合研究，涵盖可信度不同维度的原则、主流LLMs可信度的基准构建、评估与分析，以及开放挑战与未来方向的讨论。具体而言，我们首先提出一套涵盖八个不同维度的可信LLMs原则体系。基于这些原则，我们进一步构建了包含真实性、安全性、公平性、鲁棒性、隐私性和机器伦理六个维度的基准。随后，我们呈现了对TrustLLM中16个主流LLMs（涵盖30余个数据集）的评估研究。研究发现：其一，可信度与实用性（即功能有效性）总体呈正相关；其二，商用LLMs在可信度方面普遍优于多数开源模型，引发对广泛可获取的开源LLMs潜在风险的担忧，但少数开源模型已接近商用模型水平；其三，部分LLMs可能存在过度校准可信度的问题，以至于误将良性提示判定为有害内容而拒绝响应，从而损害实用性；最后，我们强调不仅模型本身需保持透明，支撑可信度的技术也应透明化——明确所采用的可信技术对于分析其有效性至关重要。