多么精妙的桥梁：多语言大语言模型在不同语言中存在相似的偏见
What an Elegant Bridge: Multilingual LLMs are Biased Similarly in Different Languages
AI总结：
该研究从语法性别视角出发，利用多语言大语言模型重现拓展经典心理语言学实验，通过形容词共现分析与二元分类器验证，发现不同语言下大语言模型的偏见模式具有相似性与跨语言迁移性。
摘要：
本文从语法性别的视角探究大语言模型（LLMs）的偏见。受心理语言学经典研究，尤其是性别对语言感知影响研究的启发，我们利用多语言LLMs重现并拓展了Boroditsky（2003）的基础实验。我们将LLMs作为研究语法性别相关心理语言学偏见的新方法，提示模型用多种语言的形容词描述名词，重点关注有语法性别的语言。我们具体考察了跨性别、跨语言的形容词共现情况，并训练了一个二元分类器，根据LLM描述名词所用的形容词预测语法性别。令人惊讶的是，我们发现简单的分类器不仅能以高于随机水平的准确率预测名词性别，还具备跨语言迁移能力。研究表明，尽管LLMs在不同语言中对词汇的描述可能存在差异，但它们的偏见模式是相似的。
英文摘要：
This paper investigates biases of Large Language Models (LLMs) through the lens of grammatical gender. Drawing inspiration from seminal works in psycholinguistics, particularly the study of gender's influence on language perception, we leverage multilingual LLMs to revisit and expand upon the foundational experiments of Boroditsky (2003). Employing LLMs as a novel method for examining psycholinguistic biases related to grammatical gender, we prompt a model to describe nouns with adjectives in various languages, focusing specifically on languages with grammatical gender. In particular, we look at adjective co-occurrences across gender and languages, and train a binary classifier to predict grammatical gender given adjectives an LLM uses to describe a noun. Surprisingly, we find that a simple classifier can not only predict noun gender above chance but also exhibit cross-language transferability. We show that while LLMs may describe words differently in different languages, they are biased similarly.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
