Understanding the effects of language-specific class imbalance in multilingual fine-tuning

• URL: http://arxiv.org/abs/2402.13016v1
• Date: Tue, 20 Feb 2024 13:59:12 GMT
• Title: Understanding the effects of language-specific class imbalance in multilingual fine-tuning
• Authors: Vincent Jung, Lonneke van der Plas
• Abstract summary: We show that fine-tuning a transformer-based Large Language Model (LLM) on a dataset with an imbalance leads to worse performance. We modify the traditional class weighing approach to imbalance by calculating class weights separately for each language.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We study the effect of one type of imbalance often present in real-life multilingual classification datasets: an uneven distribution of labels across languages. We show evidence that fine-tuning a transformer-based Large Language Model (LLM) on a dataset with this imbalance leads to worse performance, a more pronounced separation of languages in the latent space, and the promotion of uninformative features. We modify the traditional class weighing approach to imbalance by calculating class weights separately for each language and show that this helps mitigate those detrimental effects. These results create awareness of the negative effects of language-specific class imbalance in multilingual fine-tuning and the way in which the model learns to rely on the separation of languages to perform the task.

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