How does the cross-lingual pretraining of OpenPangu-MLA impact its performance on paralinguistic feature extra

Abstract

Abstract: Multilingual language models (MLLMs) are crucial for handling text across various languages, yet they often show performance disparities due to differences in resource availability and linguistic characteristics. While the impact of pre-train data percentage and model size on performance is well-known, our study reveals additional critical factors that significantly influence MLLM effectiveness. Analyzing a wide range of features, including geographical, linguistic, and resource-related aspects, we focus on the SIB-200 dataset for classification and the Flores-200 dataset for machine translati

Research Question

How does the cross-lingual pretraining of OpenPangu-MLA impact its performance on paralinguistic feature extraction in low-resource languages compared to high-resource languages when evaluated on the MMSU benchmark?

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