Large Language Models (LLMs) have remarkable capabilities across NLP tasks. However, their performance in multilingual contexts, especially within the mental health domain, has not been thoroughly explored. In this paper, we evaluate proprietary and open-source LLMs on eight mental health datasets in various languages, as well as their machine-translated (MT) counterparts. We compare LLM performance in zero-shot, few-shot, and fine-tuned settings against conventional NLP baselines that do not employ LLMs. In addition, we assess translation quality across language families and typologies to understand its influence on LLM performance. Proprietary LLMs and fine-tuned open-source LLMs achieve competitive F1 scores on several datasets, often surpassing state-of-the-art results. However, performance on MT data is generally lower, and the extent of this decline varies by language and typology. This variation highlights both the strengths of LLMs in handling mental health tasks in languages other than English and their limitations when translation quality introduces structural or lexical mismatches.

翻译：大型语言模型（LLMs）在自然语言处理任务中展现出卓越能力。然而，其在多语言环境下的表现，尤其是在心理健康领域，尚未得到充分探索。本文在涵盖多种语言的八个心理健康数据集及其机器翻译（MT）版本上，评估了专有和开源LLMs的性能。我们将LLMs在零样本、少样本和微调设置下的表现与未采用LLMs的传统自然语言处理基线方法进行比较。此外，我们评估了跨语系和语言类型的翻译质量，以理解其对LLM性能的影响。专有LLMs和经过微调的开源LLMs在多个数据集上取得了具有竞争力的F1分数，往往超越现有最佳结果。然而，在机器翻译数据上的表现普遍较低，且这种下降程度因语言和语言类型而异。这种差异既凸显了LLMs在处理英语以外的心理健康任务时的优势，也揭示了当翻译质量引入结构或词汇不匹配时的局限性。