Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis

Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. We conduct exper...

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Bibliographic Details
Published inarXiv.org
Main Authors Li, Daoyang, Jin, Mingyu, Zeng, Qingcheng, Zhao, Haiyan, Du, Mengnan
Format Paper
LanguageEnglish
Published Ithaca Cornell University Library, arXiv.org 22.09.2024
Subjects
Accuracy
English language
Large language models
Similarity
Trends
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Summary:Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. In this paper, we extend these probing methods to a multilingual context, investigating the behaviors of LLMs across diverse languages. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages. Our key findings reveal: (1) a consistent performance gap between high-resource and low-resource languages, with high-resource languages achieving significantly higher probing accuracy; (2) divergent layer-wise accuracy trends, where high-resource languages show substantial improvement in deeper layers similar to English; and (3) higher representational similarities among high-resource languages, with low-resource languages demonstrating lower similarities both among themselves and with high-resource languages. These results highlight significant disparities in LLMs' multilingual capabilities and emphasize the need for improved modeling of low-resource languages.
ISSN:2331-8422