How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models
Jiyue Jiang, Pengan Chen, Liheng Chen, Sheng Wang, Qinghang Bao, Lingpeng Kong, Yu Li, Chuan Wu · 2025
The rapid evolution of large language models (LLMs) has transformed the competitive landscape in natural language processing (NLP), particularly for English and other data-rich languages.However, underrepresented languages like Cantonese, spoken by over 85 million people, face significant development gaps, which is particularly concerning given the economic significance of the Guangdong-Hong Kong-Macau Greater Bay Area, and in substantial Cantonese-speaking populations in places like Singapore and North America.Despite its wide use, Cantonese has scant representation in NLP research, especially compared to other languages from similarly developed regions.To bridge these gaps, we outline current Cantonese NLP methods and introduce new benchmarks designed to evaluate LLM performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantonese, which aim to advance open-source Cantonese LLM technology.We also propose future research directions and recommended models to enhance Cantonese LLM development 1 .Asian Pop.Ratio European Pop.Ratio Cantonese 87 0.78 English 1456 6.57Mandarin 1138 3.80 German 133 18.41 Hindi 610 1.24 Russian 255 2.68 Arabic 376 4.82 French 310 7.34 Bengali 273 0.70 Italian 68 9.57 Persian 79 1.84 Spanish 559 2.54 Japanese 123 13.24 Polish 45 7.76 Marathi 99 1.14 Ukrainian 39 2.46 Turkish 90 4.16 Romanian 26 9.19 Korean 82 6.59 Dutch 55 8.56 Vietnamese 86 2.