Summary of Leia: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation, by Ikuya Yamada and Ryokan Ri
LEIA: Facilitating Cross-lingual Knowledge Transfer in Language Models with Entity-based Data Augmentation
by Ikuya Yamada, Ryokan Ri
First submitted to arxiv on: 18 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Machine Learning (cs.LG)
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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 study introduces LEIA, a novel language adaptation tuning method that leverages Wikipedia entity names aligned across languages to improve the performance of English-based large language models (LLMs) on non-English languages. The approach involves augmenting the target language corpus with English entity names and training the model using left-to-right language modeling. Experimental results demonstrate significant performance gains on diverse question answering datasets, showcasing the efficacy of LEIA for adapting LLMs to new languages. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about a new way to make large language models work better in other languages by using information from Wikipedia. The idea is to add English words and phrases that refer to specific things (like people or places) to the text in the target language, and then train the model to use this information. This helps the model understand the target language better and makes it more accurate when answering questions. The results show that this approach works well for many different languages. |
Keywords
* Artificial intelligence * Question answering
