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

• URL: http://arxiv.org/abs/2409.14459v1
• Date: Sun, 22 Sep 2024 14:14:05 GMT
• Title: Exploring Multilingual Probing in Large Language Models: A Cross-Language Analysis
• Authors: Daoyang Li, Mingyu Jin, Qingcheng Zeng, Haiyan Zhao, Mengnan Du,
• Abstract summary: Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of the world's languages. We conduct experiments on several open-source LLM models, analyzing probing accuracy, trends across layers, and similarities between probing vectors for multiple languages.
• Score: 19.37853222555255
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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