No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement

• URL: http://arxiv.org/abs/2404.15737v2
• Date: Sun, 8 Sep 2024 15:03:14 GMT
• Title: No Train but Gain: Language Arithmetic for training-free Language Adapters enhancement
• Authors: Mateusz Klimaszewski, Piotr Andruszkiewicz, Alexandra Birch,
• Abstract summary: We introduce a novel method called language arithmetic, which enables training-free post-processing. The effectiveness of the proposed solution is demonstrated on three downstream tasks in a MAD-X-based set of cross-lingual schemes.
• Score: 59.37775534633868
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Modular deep learning is the state-of-the-art solution for lifting the curse of multilinguality, preventing the impact of negative interference and enabling cross-lingual performance in Multilingual Pre-trained Language Models. However, a trade-off of this approach is the reduction in positive transfer learning from closely related languages. In response, we introduce a novel method called language arithmetic, which enables training-free post-processing to address this limitation. Extending the task arithmetic framework, we apply learning via addition to the language adapters, transitioning the framework from a multi-task to a multilingual setup. The effectiveness of the proposed solution is demonstrated on three downstream tasks in a MAD-X-based set of cross-lingual schemes, acting as a post-processing procedure. Language arithmetic consistently improves the baselines with significant gains, especially in the most challenging case of zero-shot application. Our code and models are available at https://github.com/mklimasz/language-arithmetic .

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