MLLMGuard：面向多模态大语言模型的多维安全评估套件
MLLMGuard: A Multi-dimensional Safety Evaluation Suite for Multimodal Large Language Models
AI总结：
本文提出 MLLMGuard 安全评估套件，构建双语图文数据集并覆盖五大安全维度，结合人工标注、红队技术与 GuardRank 评估器，对 13 个 MLLM 进行系统评测。
摘要：
在大语言模型（LLM）显著进展的推动下，多模态大语言模型（MLLM）在多种任务中展现出令人瞩目的能力。然而，MLLM 的实际应用场景十分复杂，使其暴露于潜在恶意指令之下，并因此带来安全风险。尽管现有基准确实纳入了某些安全考量，但它们往往缺乏全面覆盖，也未能表现出必要的严谨性和稳健性。例如，将 GPT-4V 同时用作评估器和待评估模型的常见做法缺乏可信度，因为它往往会对自身回答表现出偏向。本文提出 MLLMGuard，这是一个面向 MLLM 的多维安全评估套件，包含双语图文评估数据集、推理工具和轻量级评估器。MLLMGuard 的评估全面覆盖两种语言（英语和中文）以及五个重要安全维度（隐私、偏见、毒性、真实性和合法性），每个维度都有相应丰富的子任务。围绕这些维度，我们的评估数据集主要来源于社交媒体等平台，并结合基于文本和基于图像的红队技术，由人类专家进行细致标注。这可以避免使用开源数据集时因数据泄漏导致的不准确评估，并确保基准的质量和挑战性。此外，我们开发了一个名为 GuardRank 的全自动轻量级评估器，其评估准确率显著高于 GPT-4。我们对 13 个先进模型的评估结果表明，MLLM 要被认为是安全且负责任的，仍有相当长的路要走。
英文摘要：
Powered by remarkable advancements in Large Language Models (LLMs), Multimodal Large Language Models (MLLMs) demonstrate impressive capabilities in manifold tasks. However, the practical application scenarios of MLLMs are intricate, exposing them to potential malicious instructions and thereby posing safety risks. While current benchmarks do incorporate certain safety considerations, they often lack comprehensive coverage and fail to exhibit the necessary rigor and robustness. For instance, the common practice of employing GPT-4V as both the evaluator and a model to be evaluated lacks credibility, as it tends to exhibit a bias toward its own responses. In this paper, we present MLLMGuard, a multidimensional safety evaluation suite for MLLMs, including a bilingual image-text evaluation dataset, inference utilities, and a lightweight evaluator. MLLMGuard's assessment comprehensively covers two languages (English and Chinese) and five important safety dimensions (Privacy, Bias, Toxicity, Truthfulness, and Legality), each with corresponding rich subtasks. Focusing on these dimensions, our evaluation dataset is primarily sourced from platforms such as social media, and it integrates text-based and image-based red teaming techniques with meticulous annotation by human experts. This can prevent inaccurate evaluation caused by data leakage when using open-source datasets and ensures the quality and challenging nature of our benchmark. Additionally, a fully automated lightweight evaluator termed GuardRank is developed, which achieves significantly higher evaluation accuracy than GPT-4. Our evaluation results across 13 advanced models indicate that MLLMs still have a substantial journey ahead before they can be considered safe and responsible.
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。
