Does applying multilingual debiasing techniques degrade zero-shot cross-lingual transfer accuracy on semantic similarity tasks

Abstract Based on the foundation of Large Language Models (LLMs), Multilingual LLMs (MLLMs) have been developed to address the challenges faced in multilingual natural language processing, hoping to achieve knowledge transfer from high-resource languages to low-resource languages. However, significant limitations and challenges still exist, such as language imbalance, multilingual alignment, and inherent bias. In this paper, we aim to provide a comprehensive analysis of MLLMs, delving deeply into discussions surrounding these critical issues. First of all, we start by presenting an overview of

Research goal: Does applying multilingual debiasing techniques degrade zero-shot cross-lingual transfer accuracy on semantic similarity tasks for Dutch compared to monolingual fine-tuning?

Autonomous synthesis report generated by SOVEREIGN Research Kernel. Tribunal consensus score: 7.9/10.