Summary of Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models, by Lynn Chua et al.
Crosslingual Capabilities and Knowledge Barriers in Multilingual Large Language Models
by Lynn Chua, Badih Ghazi, Yangsibo Huang, Pritish Kamath, Ravi Kumar, Pasin Manurangsi, Amer Sinha, Chulin Xie, Chiyuan Zhang
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 A study evaluates the crosslingual abilities of large language models (LLMs) on inherently multilingual tasks. While these models show promise in machine translation and embedding space analyses, they struggle with deeper crosslingual knowledge transfer, revealing a crosslingual knowledge barrier. Fine-tuning LLMs on mixed-language data reduces these gaps, even when using out-of-domain datasets like WikiText. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can understand many languages because they were trained on lots of different texts in various languages. But can they really understand what words and ideas mean across languages? This study looks at how well big language models do on tasks that need them to transfer knowledge from one language to another. The results show that while the models are good at simple translation, they struggle to learn deeper concepts in a new language. To fix this problem, we suggest training the models on mixed-language data, which helps them understand language better. |
Keywords
* Artificial intelligence * Embedding space * Fine tuning * Translation
