使用GPT-4揭示概念的语义
Gaël Le Mens1, Balázs Kovács2, Michael T Hannan3
1Department of Economics and Business, Universitat Pompeu Fabra (UPF), Barcelona School of Economics (BSE), UPF-Barcelona School of Management, Barcelona 08005, Spain.
概括
像GPT-4这样的大型语言模型 (LLM) 可以测量文本的典型性,与没有训练的人类判断相匹配. 这一突破超越了以前的方法,为社会科学研究提供了一个强大的新工具.
科学领域:
- 计算社会科学 计算社会科学
- 自然语言处理自然语言处理.
- 人工智能的人工智能
背景情况:
- 最近的大型语言模型 (LLM) 展示了类似人类的文本生成能力.
- 法律法规提供了创造与人类判断一致的语义相似度的潜力.
- 典型性,一个文本与一个概念的相似性,是关键的语义指标.
研究的目的:
- 实证测试LLM在构建与人类一致的语义典型性指标方面的实用性.
- 评估GPT-4在与现有基准相比生成典型性指标方面的表现.
- 评估社会科学应用LLM的零射击学习能力.
主要方法:
- 利用GPT-4开发了一种新的典型性测量方法.
- 将GPT-4的典型性测量与之前基于最先进模型的测量进行了比较.
- 在两个不同的数据集中使用与人类典型性评级的相关性来评估表现:文学类型中的书籍描述和美国国会成员的推特.
主要成果:
- 基于GPT-4的典型性测量达到或超过了之前的最先进测量的性能.
- 这种高性能是使用零射击学习实现的,没有对研究数据进行任何培训.
- 之前的最先进的措施需要对大型语言模型进行广泛的微调.
结论:
- GPT-4可以有效地产生与人类判断一致的某种程度的文本典型性.
- 使用先进的LLM进行零射击学习,与以往社会科学中语义分析的微调方法相比,具有显著的进步.
- 这项研究验证了LLM作为社会科学家构建语义相似度的强大,高效的工具.
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