正义的尺度：关于大语言模型安全评估的全面调查
The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs
AI总结：
本文系统调研了大语言模型的安全评估现状，提出四维分类框架，分析评估原因、内容、场景及方法，指出当前挑战并提出研究方向，强调安全评估对可靠部署的重要性。
摘要：
随着人工智能的快速发展，大型语言模型（LLMs）在自然语言处理（NLP）领域展现出了显著的能力，包括内容生成、人机交互、机器翻译和代码生成。然而，其广泛应用也引发了重大的安全问题。特别是LLM生成的内容可能表现出不安全的行为，如毒性、偏见或虚假信息，尤其是在对抗性情境中，这引起了学术界和工业界越来越多的关注。尽管已有大量研究试图评估这些风险，但对LLM安全评估的全面和系统性调查仍然缺乏。本文旨在填补这一空白，通过系统回顾近期LLM安全评估的最新进展。具体而言，我们提出一个四维分类框架：（i）为何评估，探讨LLM安全评估的背景，其与一般LLM评估的不同之处以及此类评估的重要性；（ii）评估什么，考察并分类现有安全评估任务，基于关键能力进行分类，包括毒性、鲁棒性、伦理、偏见和公平性、真实性等维度；（iii）在哪里评估，总结当前安全评估中使用的评估指标、数据集和基准；（iv）如何评估，回顾现有主流评估方法，基于评估者的角色以及一些整合整个评估流程的评估框架。最后，我们识别了LLM安全评估中的挑战，并提出了有前景的研究方向，以促进该领域进一步的发展。我们强调优先考虑安全评估的必要性，以确保LLM在现实应用中的可靠和负责任的部署。
英文摘要：
With the rapid advancement of artificial intelligence, Large Language Models (LLMs) have shown remarkable capabilities in Natural Language Processing (NLP), including content generation, human-computer interaction, machine translation, and code generation. However, their widespread deployment has also raised significant safety concerns. In particular, LLM-generated content can exhibit unsafe behaviors such as toxicity, bias, or misinformation, especially in adversarial contexts, which has attracted increasing attention from both academia and industry. Although numerous studies have attempted to evaluate these risks, a comprehensive and systematic survey on safety evaluation of LLMs is still lacking. This work aims to fill this gap by presenting a structured overview of recent advances in safety evaluation of LLMs. Specifically, we propose a four-dimensional taxonomy: (i) Why to evaluate, which explores the background of safety evaluation of LLMs, how they differ from general LLMs evaluation, and the significance of such evaluation; (ii) What to evaluate, which examines and categorizes existing safety evaluation tasks based on key capabilities, including dimensions such as toxicity, robustness, ethics, bias and fairness, truthfulness, and related aspects; (iii) Where to evaluate, which summarizes the evaluation metrics, datasets and benchmarks currently used in safety evaluations; (iv) How to evaluate, which reviews existing mainstream evaluation methods based on the roles of the evaluators and some evaluation frameworks that integrate the entire evaluation pipeline. Finally, we identify the challenges in safety evaluation of LLMs and propose promising research directions to promote further advancement in this field. We emphasize the necessity of prioritizing safety evaluation to ensure the reliable and responsible deployment of LLMs in real-world applications.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
