Summary of Optimizing Language Augmentation For Multilingual Large Language Models: a Case Study on Korean, by Changsu Choi et al.
Optimizing Language Augmentation for Multilingual Large Language Models: A Case Study on Korean
by ChangSu Choi, Yongbin Jeong, Seoyoon Park, InHo Won, HyeonSeok Lim, SangMin Kim, Yejee Kang, Chanhyuk Yoon, Jaewan Park, Yiseul Lee, HyeJin Lee, Younggyun Hahm, Hansaem Kim, KyungTae Lim
First submitted to arxiv on: 16 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The paper proposes three strategies to enhance the performance of large language models (LLMs) for less-resourced languages (LRLs). By expanding MLLM vocabularies, using bilingual data for pretraining, and constructing a high-quality small-scale instruction dataset for instruction-tuning, the authors demonstrate improved results on eight tasks. The proposed Bllossom model outperforms previously developed Korean monolingual models in both quantitative and qualitative evaluations. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper helps make language models better for languages that don’t have as much data or resources. It does this by giving the models more words to use, teaching them from bilingual texts, and providing instructions on what to do. The results show that these techniques work well, especially when compared to previous attempts at making Korean language models. |
Keywords
» Artificial intelligence » Instruction tuning » Pretraining
