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English(EN) The Model as One Rater Among Several: Measuring Political Positions in Data-Sparse Regions with a Language-Model Panel

新方法利用LLM小组衡量数据稀疏地区政治立场

一项新的研究论文提出了一种新颖的方法,通过将大型语言模型视为小组中的个体评分者,来衡量数据有限地区的政治立场。该方法旨在克服现有工具主要在西方政治体系上得到验证的局限性。研究发现,添加轴定义可以提高评分者之间的一致性,在来自不同实验室的九个模型中达到了0.86的Krippendorff alpha系数。该论文还强调,模型之间的分歧可能具有信息量,特别是关于行为者对其国家基本秩序的立场,这表明解释在这些分歧中起着重要作用。 AI

影响 该方法可以实现对资源匮乏地区更准确的政治分析,改善跨文化研究和理解。

排序理由 研究论文,详细介绍了使用LLM衡量政治立场的新方法。[lever_c_demoted from research: ic=1 ai=1.0]

在 arXiv cs.CL 阅读 →

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新方法利用LLM小组衡量数据稀疏地区政治立场

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研究论文,详细介绍了使用LLM衡量政治立场的新方法。[lever_c_demoted from research: ic=1 ai=1.0]
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报道来源 [1]

  1. arXiv cs.CL TIER_1 English(EN) · Tarek Gara ·

    模型作为多位评估者之一:利用语言模型小组衡量数据稀疏地区政治立场

    Most tools for measuring political positions, manifesto coding, expert surveys, text-scaling models, were built and validated on Western party systems, and outside that setting they work poorly, and often not at all. This paper is an attempt at a method for those settings. It tre…