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
Abstract
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.
BibTeX
@inproceedings{jiang-etal-2025-well,
title = "How Well Do {LLM}s Handle {C}antonese? Benchmarking {C}antonese Capabilities of Large Language Models",
author = "Jiang, Jiyue and
Chen, Pengan and
Chen, Liheng and
Wang, Sheng and
Bao, Qinghang and
Kong, Lingpeng and
Li, Yu and
Wu, Chuan",
editor = "Chiruzzo, Luis and
Ritter, Alan and
Wang, Lu",
booktitle = "Findings of the Association for Computational Linguistics: NAACL 2025",
month = apr,
year = "2025",
address = "Albuquerque, New Mexico",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.findings-naacl.253/",
pages = "4464--4505",
ISBN = "979-8-89176-195-7"
}