自适应数据集构建用于现实世界多模态安全场景
Self-adaptive Dataset Construction for Real-World Multimodal Safety Scenarios
AI总结：
本文提出了一种图像导向的自适应数据集构建方法，用于构建现实世界多模态安全场景的数据集，通过生成35,000个图像-文本对及其指导响应，提升了安全评估的有效性。
摘要：
多模态大语言模型（MLLMs）正在迅速发展，呈现出日益复杂的安全挑战。然而，当前的数据集构建方法，以风险为导向，未能覆盖现实世界多模态安全场景（RMS）日益增长的复杂性。由于缺乏统一的评估指标，其整体有效性仍未得到证实。本文介绍了一种新的图像导向的自适应数据集构建方法用于RMS，该方法从图像开始，最终构建配对的文本和指导响应。使用图像导向的方法，我们自动生成了一个包含35,000个图像-文本对及其指导响应的RMS数据集。此外，我们引入了一个标准化的安全数据集评估指标：微调一个安全判断模型并在其他安全数据集上评估其能力。在各种任务上的广泛实验证明了所提出图像导向流程的有效性。结果证实了图像导向方法的可扩展性和有效性，为现实世界多模态安全数据集的构建提供了新的视角。该数据集在https://huggingface.co/datasets/NewCityLetter/RMS2/tree/main上提供。
英文摘要：
Multimodal large language models (MLLMs) are rapidly evolving, presenting increasingly complex safety challenges. However, current dataset construction methods, which are risk-oriented, fail to cover the growing complexity of real-world multimodal safety scenarios (RMS). And due to the lack of a unified evaluation metric, their overall effectiveness remains unproven. This paper introduces a novel image-oriented self-adaptive dataset construction method for RMS, which starts with images and end constructing paired text and guidance responses. Using the image-oriented method, we automatically generate an RMS dataset comprising 35k image-text pairs with guidance responses. Additionally, we introduce a standardized safety dataset evaluation metric: fine-tuning a safety judge model and evaluating its capabilities on other safety datasets.Extensive experiments on various tasks demonstrate the effectiveness of the proposed image-oriented pipeline. The results confirm the scalability and effectiveness of the image-oriented approach, offering a new perspective for the construction of real-world multimodal safety datasets. The dataset is presented at https://huggingface.co/datasets/NewCityLetter/RMS2/tree/main.
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。
