Abstract
Recently, instruction-tuned large language models (LLMs) are showing prominent performance on various tasks, such as question answering. However, the majority of instruction-tuned LLMs are English-centric, which hinders their application to low-resource language QA. In this paper, we propose COde-Mixed Multilingual Instruction Tuning (COMMIT) to adapt English-centric LLM to low-resource language QA. We point out two main causes of English-centricness: imbalance of unlabeled data, and English-centric instruction tuning datasets. To deviate from English-centric instruction tuning, we propose to specialize code-mixing for instruction tuning, which blocks code-mixing in English templates, to leverage the potential of its superiority. To overcome data imbalance, we perform cross-lingual alignment. The majority of cross-lingual alignment works focused on making representations similar, which is not desirable to decoder-based LLMs, such as LLaMA. Therefore, we propose code-mixed continual causal language modeling to align the decoder. COMMIT improves the exact match score of low-resourced language QA by up to 32x. Code is publicly available.- Anthology ID:
- 2024.findings-naacl.198
- Volume:
- Findings of the Association for Computational Linguistics: NAACL 2024
- Month:
- June
- Year:
- 2024
- Address:
- Mexico City, Mexico
- Editors:
- Kevin Duh, Helena Gomez, Steven Bethard
- Venue:
- Findings
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 3130–3137
- Language:
- URL:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-naacl.198/
- DOI:
- 10.18653/v1/2024.findings-naacl.198
- Cite (ACL):
- Jaeseong Lee, YeonJoon Jung, and Seung-won Hwang. 2024. COMMIT: Code-Mixing English-Centric Large Language Model for Multilingual Instruction Tuning. In Findings of the Association for Computational Linguistics: NAACL 2024, pages 3130–3137, Mexico City, Mexico. Association for Computational Linguistics.
- Cite (Informal):
- COMMIT: Code-Mixing English-Centric Large Language Model for Multilingual Instruction Tuning (Lee et al., Findings 2024)
- PDF:
- https://preview.aclanthology.org/add_missing_videos/2024.findings-naacl.198.pdf