研究写作风格如何影响大模型嵌入向量的分散程度。
Embedding Style Beyond Topics: Analyzing Dispersion Effects Across Different Language Models
- 用风格与主题交替的文学语料测试模型
- 发现写作风格显著影响嵌入空间分布
- 适合关注模型可解释性的研究人员
本文分析了写作风格如何影响多个先进语言模型中嵌入向量的分散程度。早期Transformer模型主要与主题建模对齐，而本研究重点考察写作风格在塑造嵌入空间中的作用。采用交替切换主题与风格的文学语料库，比较法语与英语模型的敏感性差异。通过分析风格对嵌入分散的具体影响，旨在更深入理解语言模型对风格信息的处理机制，从而提升其整体可解释性。
原文摘要 · Abstract (English)
This paper analyzes how writing style affects the dispersion of embedding vectors across multiple, state-of-the-art language models. While early transformer models primarily aligned with topic modeling, this study examines the role of writing style in shaping embedding spaces. Using a literary corpus that alternates between topics and styles, we compare the sensitivity of language models across French and English. By analyzing the particular impact of style on embedding dispersion, we aim to better understand how language models process stylistic information, contributing to their overall interpretability.
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