Benchmarking Multilingual LLM Translation Accuracy for Fuzhounese

Multilingual large language models are known to perform very well on high-resource languages, while their ability to process severely under-resourced languages remains underexplored. We investigate multilingual LLM translation performance on Fuzhounese, an under-resourced Sinitic language without a standardized orthography and almost no digital presence. Having adopted some methodological insights from the HKCanto-Eval benchmark, this paper presents a bidirectional translation framework based on a dataset of 305 sentences (300 constructed English sentences and 5 additional reference translations), that assesses the comprehension and generation of Fuzhounese, evaluated using automatic metrics and human Likert-scale judgments. The results reveal poor performance on Fuzhounese in both translation directions: BERTScore and chrF++ values consistently stay low when models are faced with comprehension tasks, while for generation tasks, scores are generally more than twofold lower than those for Mandarin or Cantonese. These findings highlight structural biases in multilingual LLMs toward high-resource languages and stress the need for resource-aware modeling and evaluation approaches in multilingual NLP systems.