多模式的大型语言模型可以做出情境敏感的仇恨言论评估,与人类判断保持一致
1Department of Sociology, Rutgers University-New Brunswick, New Brunswick, NJ, USA. thomas.davidson@rutgers.edu.
Nature human behaviour
|December 16, 2025
概括
多模式大语言模型 (MLLMs) 显示出内容调节的前景,与人类对仇恨言论的判断保持一致. 然而,偏见仍然存在,特别是在较小的模型中,突出了人工智能审计的风险和好处.
科学领域:
- 人工智能的人工智能
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
背景情况:
- 自动化内容调节与上下文作斗争.
- 多模式大语言模型 (MLLMs) 提供了提高准确性的潜力.
- 评估MLLM在复杂任务中的表现,如仇恨言论的检测至关重要.
研究的目的:
- 调查MLLM如何评估仇恨言论.
- 用人类判断来比较MLLM的表现.
- 在MLLM仇恨言论检测中识别偏见和上下文敏感性.
主要方法:
- 展示模拟社交媒体帖子的联合实验.
- 属性的系统变化,如污名化使用和用户人口统计.
- 与1854名人类参与者对比MLLM决策.
主要成果:
- 更大,更先进的MLLM展示了情境敏感的仇恨言论评估,与人类判断保持一致.
- 观察到持续的人口和词汇偏差,特别是在较小的模型中.
- 语境敏感性随着提示而改善,但并没有完全消除;视觉线索影响了一些模型.
结论:
- 对于内容调节来说,MLLM提供了好处,但由于固有的偏见,它存在风险.
- 联合实验对于在上下文依赖的应用中对AI进行审计是有效的.
- 需要进一步的研究来缓解偏见,并提高MLLM对内容调节的可靠性.
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