LLM Evaluation Complexities for Non-Latin Languages
Blog post from Comet
Large language models (LLMs) have dramatically advanced natural language processing, but their development has predominantly focused on Latin-script languages, posing significant challenges when applied to non-Latin scripts like Chinese, Japanese, and Korean (CJK). These challenges are multifaceted, involving character-level complexities such as tokenization without spaces and vast character sets, language-level obstacles like adapting masking strategies and model architectures, and cultural-level hurdles in evaluation metrics and benchmarks that don't align with traditional English-centric methods. Innovations have emerged, such as character decomposition and specialized models like Lattice-BERT, to better handle the unique linguistic and structural features of CJK languages. Efforts to create new benchmarks like CLUE, JGLUE, and KLUE are crucial for meaningful progress in CJK NLP, highlighting the necessity for tailored evaluation methods that respect these languages' structural and cultural nuances.
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