Language model fine-tuned to translate space-less Khmer language

A language model was fine-tuned to translate Khmer, a language that lacks spaces between words, using a dataset of 8,000 sentences and a single GPU. The process involved adapting tokenization methods like WordPiece and byte-pair encoding, which are typically used for languages with spaces such as Standard Chinese, English, and Japanese. This experiment explored the capabilities of models like GPT-3 and Bert in handling such linguistic challenges. AI

IMPACT Demonstrates adaptability of LLMs to languages without spaces, potentially improving translation for under-resourced languages.

RANK_REASON The item describes research into fine-tuning a language model for a specific linguistic challenge (translating a space-less language), including technical details on tokenization methods. [lever_c_demoted from research: ic=1 ai=1.0]

Read on Medium — fine-tuning tag →

• Bert
• byte-pair encoding
• English
• GPT-3
• Japanese
• Khmer
• language model
• Standard Chinese
• Transformer++
• WordPiece

• model release
• paper

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