How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models
Jan 1, 2025·,,
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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. This paper introduces new benchmarks designed to evaluate LLM performance in factual generation, mathematical logic, complex reasoning, and general knowledge in Cantonese.
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In The 2025 Annual Conference of the Nations of the Americas Chapter of the ACL
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Sheng Wang
(Forence)
PhD Graduate in Computer Science
Sheng Wang is a PhD graduate from The University of Hong Kong, supervised by Prof. Chuan Wu and Prof. Lingpeng Kong.
His research focuses on Agent, LLM Super-Alignment, and Data Synthesis. He has published 14+ papers in top-tier
conferences including NIPS2025 (Spotlight), ICLR2025, ACL2024/2025, EMNLP2025.
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