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

2025-06-06

AI Summary

The field of safety evaluation for large language models (LLMs) lacks a systematic, unified survey. Method: This paper introduces the first holistic “Why/What/Where/How” four-dimensional analytical framework to rigorously distinguish safety evaluation from general model evaluation. Through bibliometric analysis, cross-methodological comparison, and taxonomy-driven modeling, it systematically synthesizes over 100 studies spanning core safety dimensions—including toxicity, bias, robustness, and truthfulness—and integrates diverse paradigms such as human evaluation, LLM-as-a-judge automation, red-teaming, and adversarial prompt engineering. Contributions: (1) A novel multi-granularity knowledge graph of LLM safety evaluation; (2) A structured resource inventory comprising 30+ benchmarks, 50+ metrics, and 20+ tools; and (3) Identification of key open challenges alongside reusable methodological pathways—providing both theoretical foundations and practical guidance for academic research and industrial deployment.

Technology Category

• Natural Language Processing: Safety and Robustness
• Machine Learning: Large Multimodal Models (LMMs)
• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• User Modeling, Personalization and Recommendation: Large Language Models (LLM) for user modeling and recommendation
• Security and Privacy: Large-scale security measurements

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.

Problem

• Systematically review safety evaluation methods for LLMs
• Address toxicity, bias, and robustness in LLM outputs
• Identify challenges and future directions in LLM safety

Innovation

• Systematic review of LLMs safety evaluation advancements
• Categorizes safety tasks: toxicity, robustness, ethics, bias
• Summarizes metrics, datasets, toolkits for evaluation methods