Investigating the Potential of Task Arithmetic for Cross-Lingual Transfer

AbstractCross-lingual transfer has recently been tackled through modular, parameter-efficient fine-tuning methods which allow arbitrary combinations of language and task modules for transfer of any task to any language. Concurrently, task arithmetic has emerged as a powerful and modular tool for editing pretrained models using multiple full fine-tunings. In this work, we connect the paradigms of task arithmetic and cross-lingual transfer, demonstrating that modularity for cross-lingual transfer can be achieved even with full model fine-tuning. Our approach displays strong performance on a range of multilingual benchmarks encompassing both high-resource and low-resource languages.