Recent research explores the limitations of Language Model Models (LLMs) in non-English languages due to their pretraining on English-dominant data. It focuses on transferring language generation capabilities and instruction-following to non-English languages using LLaMA, revealing that vocabulary extension is unnecessary and effective transfer can be achieved with minimal pretraining data.
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Improving Non-English Language Model Proficiency
Challenges with Existing Language Models
Many language models, like ChatGPT and LLaMA, are primarily trained on English text, limiting their performance in non-English languages.
Advancements and Limitations
New language models like ChatGPT, PaLM, and LLaMA show advanced capabilities, but imbalanced language resources pose challenges.
Research into Non-English Language Transfer
Researchers at Fudan University have focused on transferring language generation capabilities to non-English languages, achieving state-of-the-art performance with minimal pretraining data.
Study Findings
The study found that vocabulary extension is unnecessary for effective transfer of language generation capabilities to non-English languages, and comparable performance can be achieved with minimal pretraining data.
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