Summary of From English-centric to Effective Bilingual: Llms with Custom Tokenizers For Underrepresented Languages, by Artur Kiulian et al.
From English-Centric to Effective Bilingual: LLMs with Custom Tokenizers for Underrepresented Languages
by Artur Kiulian, Anton Polishko, Mykola Khandoga, Yevhen Kostiuk, Guillermo Gabrielli, Łukasz Gagała, Fadi Zaraket, Qusai Abu Obaida, Hrishikesh Garud, Wendy Wing Yee Mak, Dmytro Chaplynskyi, Selma Belhadj Amor, Grigol Peradze
First submitted to arxiv on: 24 Oct 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 This paper presents a cost-effective approach to developing bilingual large language models (LLMs) that can support English and any target language. The proposed method involves vocabulary expansion, initialization of new embeddings, model training, and evaluation. The authors demonstrate the effectiveness of their approach by conducting experiments with three languages – Ukrainian, Arabic, and Georgian – each using a non-Latin script. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about creating special kinds of computer models that can understand two languages: English and another language you choose. Right now, these models are hard to make because they need lots of data and training. The authors came up with a way to make them more efficient by adding new words and ideas from the target language. They tested their method on three different languages – Ukrainian, Arabic, and Georgian – and showed that it works! |
