Adapting Mental Health Prediction Tasks for Cross-lingual Learning via
  Meta-Training and In-context Learning with Large Language Model
Zita Lifelo Huansheng Ning Sahraoui Dhelim
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.