概括
大型语言模型 (LLM) 对社会科学研究中的通用立场检测有希望. 性能因提示符,模型和特定语句而异,突出显示了对特定领域评估的需求.
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.
- 政治话语分析 分析政治话语
背景情况:
- 姿势检测对于理解社会话语至关重要,但对于自动化文本分析仍然具有挑战性.
- 现有的方法与隐含性和上下文等语义细微差别作斗争.
- 大型语言模型 (LLM) 提供了通用立场检测的潜力.
研究的目的:
- 探索LLM在没有域或语句特异性的通用立场检测中的有效性.
- 使用LLMs测试各种提示链进行零射击立场分类.
- 评估从可持续城市运输话语中对现实世界数据的LLM绩效.
主要方法:
- 使用了关于组织实体的1710个德国报纸段落的数据集.
- 标注了实体对五个规范性陈述的立场.
- 通过使用不同的提示链进行零射击分类,比较了四个公开可用的LLM的性能.
主要成果:
- 在普遍的姿势检测中,LLM表现出了足够的性能.
- 绩效受到提示链方法,使用的特定LLM和目标声明的显著影响.
- 结果表明,在不同的陈述和模型中,LLM的有效性存在差异.
结论:
- 虽然LLM显示了通用姿势检测的潜力,但性能并不均.
- 强调对LLMs领域特定评估数据集的关键需求.
- 建议仔细考虑LLM与传统方法的权衡以及它们固有的局限性.
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