Abstract
Timely identification is essential for the efficient handling of mental health illnesses such as depression. However, the current research fails to adequately address the prediction of mental health conditions from social media data in low-resource African languages like Swahili. This study introduces two distinct approaches utilising model-agnostic meta-learning and leveraging large language models (LLMs) to address this gap. Experiments are conducted on three datasets translated to low-resource language and applied to four mental health tasks, which include stress, depression, depression severity and suicidal ideation prediction. we first apply a meta-learning model with self-supervision, which results in improved model initialisation for rapid adaptation and cross-lingual transfer. The results show that our meta-trained model performs significantly better than standard fine-tuning methods, outperforming the baseline fine-tuning in macro F1 score with 18\% and 0.8\% over XLM-R and mBERT. In parallel, we use LLMs' in-context learning capabilities to assess their performance accuracy across the Swahili mental health prediction tasks by analysing different cross-lingual prompting approaches. Our analysis showed that Swahili prompts performed better than cross-lingual prompts but less than English prompts. Our findings show that in-context learning can be achieved through cross-lingual transfer through carefully crafted prompt templates with examples and instructions.
Abstract (translated)
及时的疾病诊断对于处理诸如抑郁症等心理健康疾病至关重要。然而，当前的研究未能充分关注低资源非洲语言（如斯瓦希里语）中从社交媒体数据预测精神健康状况。本研究介绍了一种利用模型无关元学习以及大型语言模型（LLMs）来解决这一问题的独特方法。实验在三种翻译至低资源语言的数据集上进行，并应用于四种精神健康任务，包括压力、抑郁、抑郁严重程度和自杀观念预测。我们首先应用了一种具有自监督的元学习模型，结果是提高了模型初始化以实现快速适应和跨语言转移。结果显示，我们的元训练模型在标准微调方法中表现得比基线微调好得多，在XLM-R和mBERT上的宏观F1得分分别比18\%和0.8\%更高。 同时，我们利用LLMs的上下文学习能力对斯瓦希里心理健康预测任务的不同跨语言提示方法进行分析。我们的分析显示，斯瓦希里提示表现更好，但不如英语提示。我们的研究结果表明，通过精心制作具有示例和说明的跨语言提示模板，可以实现上下文学习。
URL
https://arxiv.org/abs/2404.09045
