Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model

arXiv:2404.09045v1 Announce Type: cross
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, …

abstract arxiv context cross-lingual cs.ai cs.cl cs.cy cs.lg current data depression health health conditions however identification in-context learning language language model languages large language large language model low media media data mental health meta prediction research social social media social media data tasks training type via