How Well Do LLMs Handle Cantonese? Benchmarking Cantonese Capabilities of Large Language Models

TL;DR

This paper benchmarks the performance of large language models in handling Cantonese, highlighting existing gaps and proposing new evaluation benchmarks to improve Cantonese NLP capabilities.

Contribution

It introduces new benchmarks for evaluating Cantonese LLMs across multiple tasks and discusses strategies to advance open-source Cantonese NLP models.

Findings

Cantonese is underrepresented in NLP research.

New benchmarks evaluate factual, logical, and reasoning skills in Cantonese.

Recommendations for improving Cantonese LLM development.

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.