Large Language Model Understands Chinese Better with Mega Tokenization DOI Creative Commons

Xinyu Lu,

Qizhen Wang,

Xian Liu

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 10, 2024

Abstract The rapid evolution of natural language processing has seen significant advancements in models, particularly for languages with simpler orthographies. However, challenges persist accurately and understanding complex morphological structures, such as Chinese, due to the limitations traditional tokenization methods. Introducing mega tokenization, which involves significantly larger tokens, represents a novel transformative approach that enhances semantic preservation contextual coherence sophisticated character sequences. study compares performance an adapted model against standard model, demonstrating substantial improvements across tasks machine translation, text summarisation, question answering. Through rigorous evaluation statistical analysis, shows superior metrics, indicating effectiveness addressing unique posed by Chinese language. implications this extend various applications, underscoring its potential revolutionise multilingual high-stakes environments. Future research directions are proposed further optimise expand applicability diverse linguistic contexts.

Language: Английский

Large Language Model Understands Chinese Better with Mega Tokenization DOI Creative Commons

Xinyu Lu,

Qizhen Wang,

Xian Liu

et al.

Research Square (Research Square), Journal Year: 2024, Volume and Issue: unknown

Published: June 10, 2024

Abstract The rapid evolution of natural language processing has seen significant advancements in models, particularly for languages with simpler orthographies. However, challenges persist accurately and understanding complex morphological structures, such as Chinese, due to the limitations traditional tokenization methods. Introducing mega tokenization, which involves significantly larger tokens, represents a novel transformative approach that enhances semantic preservation contextual coherence sophisticated character sequences. study compares performance an adapted model against standard model, demonstrating substantial improvements across tasks machine translation, text summarisation, question answering. Through rigorous evaluation statistical analysis, shows superior metrics, indicating effectiveness addressing unique posed by Chinese language. implications this extend various applications, underscoring its potential revolutionise multilingual high-stakes environments. Future research directions are proposed further optimise expand applicability diverse linguistic contexts.

Language: Английский

Citations

3