格拉德偏差:揭示文字对文字对图像生成模型中的偏差的影响
概括
本研究引入了一个新的框架,用于检测和解释没有预定义类别的文本到图像模型中的偏差. 它使用大型语言模型和视觉问题回答来识别和量化偏见,提高AI图像生成中的公平性.
科学领域:
- 人工智能的人工智能
- 计算机视觉 计算机视觉
- 自然语言处理自然语言处理.
背景情况:
- 文本到图像 (T2I) 模型产生高质量的图像,但由于潜在的偏见,引起了对公平性和安全性的担忧.
- 现有的偏差检测方法专注于预定义的偏差的封闭集,限制其范围.
研究的目的:
- 提出一个一般的框架,用于在开放式设置中识别,量化和解释T2I模型中的偏差.
- 开发不需要预定义偏差列表的方法.
主要方法:
- 一个利用大型语言模型 (LLM) 提出标题的潜在偏差的管道.
- 使用目标T2I模型与生成的标题生成图像.
- 使用视觉问题答案 (VQA) 进行偏差评估.
- 两个框架变体:OpenBias用于检测/定量化,GradBias用于提示文字贡献分析.
主要成果:
- OpenBias有效地检测到人们,物体和动物之间的已知和新的偏见,与现有的方法和人类判断保持一致.
- 格拉德比亚斯揭示了中立词语可以显著影响偏见.
- 无论是OpenBias还是GradBias,都超过了几种基线方法,包括最先进的基础模型.
结论:
- 拟议的开放式框架为T2I模型中偏差检测提供了更全面的方法.
- 了解提示词的影响对于减轻意外偏见至关重要.
- 这项工作有助于开发更公平,更安全的生成人工智能技术.
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