The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs

• URL: http://arxiv.org/abs/2506.11094v1
• Date: Fri, 06 Jun 2025 05:50:50 GMT
• Title: The Scales of Justitia: A Comprehensive Survey on Safety Evaluation of LLMs
• Authors: Songyang Liu, Chaozhuo Li, Jiameng Qiu, Xi Zhang, Feiran Huang, Litian Zhang, Yiming Hei, Philip S. Yu,
• Abstract summary: Large Language Models (LLMs) have demonstrated remarkable potential in the field of Natural Language Processing (NLP)<n>LLMs have occasionally exhibited unsafe elements like toxicity and bias, particularly in adversarial scenarios.<n>This survey aims to provide a comprehensive and systematic overview of recent advancements in LLMs safety evaluation.
• Score: 42.57873562187369
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the rapid advancement of artificial intelligence technology, Large Language Models (LLMs) have demonstrated remarkable potential in the field of Natural Language Processing (NLP), including areas such as content generation, human-computer interaction, machine translation, and code generation, among others. However, their widespread deployment has also raised significant safety concerns. In recent years, LLM-generated content has occasionally exhibited unsafe elements like toxicity and bias, particularly in adversarial scenarios, which has garnered extensive attention from both academia and industry. While numerous efforts have been made to evaluate the safety risks associated with LLMs, there remains a lack of systematic reviews summarizing these research endeavors. This survey aims to provide a comprehensive and systematic overview of recent advancements in LLMs safety evaluation, focusing on several key aspects: (1) "Why evaluate" that explores the background of LLMs safety evaluation, how they differ from general LLMs evaluation, and the significance of such evaluation; (2) "What to evaluate" that examines and categorizes existing safety evaluation tasks based on key capabilities, including dimensions such as toxicity, robustness, ethics, bias and fairness, truthfulness, and so on; (3) "Where to evaluate" that summarizes the evaluation metrics, datasets and benchmarks currently used in safety evaluations; (4) "How to evaluate" that reviews existing evaluation toolkit, and categorizing mainstream evaluation methods based on the roles of the evaluators. Finally, we identify the challenges in LLMs safety evaluation and propose potential research directions to promote further advancement in this field. We emphasize the importance of prioritizing LLMs safety evaluation to ensure the safe deployment of these models in real-world applications.

Related papers

• A Survey of Safety on Large Vision-Language Models: Attacks, Defenses and Evaluations [127.52707312573791]
  This survey provides a comprehensive analysis of LVLM safety, covering key aspects such as attacks, defenses, and evaluation methods.<n>We introduce a unified framework that integrates these interrelated components, offering a holistic perspective on the vulnerabilities of LVLMs.<n>We conduct a set of safety evaluations on the latest LVLM, Deepseek Janus-Pro, and provide a theoretical analysis of the results.
  arXiv  Detail & Related papers  (2025-02-14T08:42:43Z)
• SafeBench: A Safety Evaluation Framework for Multimodal Large Language Models [75.67623347512368]
  We propose toolns, a comprehensive framework designed for conducting safety evaluations of MLLMs. Our framework consists of a comprehensive harmful query dataset and an automated evaluation protocol. Based on our framework, we conducted large-scale experiments on 15 widely-used open-source MLLMs and 6 commercial MLLMs.
  arXiv  Detail & Related papers  (2024-10-24T17:14:40Z)
• Exploring Advanced Methodologies in Security Evaluation for LLMs [16.753146059652877]
  Large Language Models (LLMs) represent an advanced evolution of earlier, simpler language models. They boast enhanced abilities to handle complex language patterns and generate coherent text, images, audios, and videos. Rapid expansion of LLMs has raised security and ethical concerns within the academic community.
  arXiv  Detail & Related papers  (2024-02-28T01:32:58Z)
• CPSDBench: A Large Language Model Evaluation Benchmark and Baseline for Chinese Public Security Domain [21.825274494004983]
  This study aims to construct a specialized evaluation benchmark tailored to the Chinese public security domain--CPSDbench. CPSDbench integrates datasets related to public security collected from real-world scenarios. This study introduces a set of innovative evaluation metrics designed to more precisely quantify the efficacy of LLMs in executing tasks related to public security.
  arXiv  Detail & Related papers  (2024-02-11T15:56:03Z)
• Evaluating Large Language Models: A Comprehensive Survey [41.64914110226901]
  Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They could suffer from private data leaks or yield inappropriate, harmful, or misleading content. To effectively capitalize on LLM capacities as well as ensure their safe and beneficial development, it is critical to conduct a rigorous and comprehensive evaluation.
  arXiv  Detail & Related papers  (2023-10-30T17:00:52Z)
• CValues: Measuring the Values of Chinese Large Language Models from Safety to Responsibility [62.74405775089802]
  We present CValues, the first Chinese human values evaluation benchmark to measure the alignment ability of LLMs. As a result, we have manually collected adversarial safety prompts across 10 scenarios and induced responsibility prompts from 8 domains. Our findings suggest that while most Chinese LLMs perform well in terms of safety, there is considerable room for improvement in terms of responsibility.
  arXiv  Detail & Related papers  (2023-07-19T01:22:40Z)
• A Survey on Evaluation of Large Language Models [87.60417393701331]
  Large language models (LLMs) are gaining increasing popularity in both academia and industry. This paper focuses on three key dimensions: what to evaluate, where to evaluate, and how to evaluate.
  arXiv  Detail & Related papers  (2023-07-06T16:28:35Z)
• Safety Assessment of Chinese Large Language Models [51.83369778259149]
  Large language models (LLMs) may generate insulting and discriminatory content, reflect incorrect social values, and may be used for malicious purposes. To promote the deployment of safe, responsible, and ethical AI, we release SafetyPrompts including 100k augmented prompts and responses by LLMs.
  arXiv  Detail & Related papers  (2023-04-20T16:27:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.