Abstract
Multilingual large language models (MLLMs), trained on multilingual balanced data, demonstrate better zero-shot learning performance in non-English languages compared to large language models trained on English-dominant data. However, the disparity in performance between English and non-English languages remains a challenge yet to be fully addressed. This study introduces a promising direction for enhancing non-English performance through a specialized pruning approach. Specifically, we prune MLLMs using bilingual sentence pairs from English and other languages and empirically demonstrate that this pruning strategy can enhance the MLLMs’ performance in non-English language.- Anthology ID:
- 2024.findings-emnlp.580
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2024
- Month:
- November
- Year:
- 2024
- Address:
- Miami, Florida, USA
- Editors:
- Yaser Al-Onaizan, Mohit Bansal, Yun-Nung Chen
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 9921–9942
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.580/
- DOI:
- 10.18653/v1/2024.findings-emnlp.580
- Cite (ACL):
- Hwichan Kim, Jun Suzuki, Tosho Hirasawa, and Mamoru Komachi. 2024. Pruning Multilingual Large Language Models for Multilingual Inference. In Findings of the Association for Computational Linguistics: EMNLP 2024, pages 9921–9942, Miami, Florida, USA. Association for Computational Linguistics.
- Cite (Informal):
- Pruning Multilingual Large Language Models for Multilingual Inference (Kim et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-emnlp.580.pdf