探索大型语言模型如何在假设性情境中理解性交流
Laura M Vowels1,2, Tiffany Marcantonio3
1School of Psychology, University of Roehampton.
Journal of sex research
|September 2, 2025
概括
大型语言模型 (LLM) 可以明确性同意和拒绝的线索. 然而,LLM在涉及中毒的细微场景中扎,强调需要在性健康应用中改进人工智能培训.
科学领域:
- 人工智能
- 性健康问题
- 人与计算机的交互
背景情况:
- 大型语言模型 (LLM) 对于信息检索越来越受欢迎.
- 它们在性健康领域的应用,尤其是在同意和拒绝方面,正在出现.
- 了解法学士在解释性交流方面的能力至关重要.
研究的目的:
- 评估在假设场景中LLM如何识别性同意和拒绝.
- 为了比较LLM对同意的解释与专家的判断.
- 在理解细微的性交流方面评估LLM的表现.
主要方法:
- 四名LLM提供了七个操纵性场景.
- 模型根据沟通线索和背景评估同意,并提供解释.
- 这项研究与八位基于性别的暴力专家的评估进行了比较.
主要成果:
- 在有明确口头沟通的场景中,LLM准确地识别了同意和拒绝,与专家的共识保持一致.
- 当场景涉及醉酒时出现差异,影响了LLM对同意的理解.
- 在解释复杂的,取决于背景的性交流方面,
结论:
- 虽然LLM在识别明确的同意线索方面表现出熟练,
- 专家不同意LLM关于与中毒有关的同意, 这强调了AI的局限性.
- 提高对微妙的人类行为的LLM培训对于可靠的性健康应用至关重要.
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