Year: 2025
Type: conference-paper
Abstract: Probing techniques for large language models (LLMs) have primarily focused on English, overlooking the vast majority of other world's languages.In this paper, we extend these probing methods to a multilingual context, investigating how LLMs encode linguistic structures 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 lowresource languages demonstrating lower similarities both among themselves and with highresource languages.These results provide insights into how linguistic structures are represented differently across languages in LLMs and emphasize the need for improved structure modeling for low-resource languages.
Authors: Daoyang Li, Haiyan Zhao, Qingcheng Zeng, Mengnan Du
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Funder: National Science Foundation
OpenAlex ID: W4412944466