通过元训练与大语言模型上下文学习适配心理健康预测任务以实现跨语言学习
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model
AI总结：
针对低资源非洲语言社交媒体心理健康预测研究不足的问题，本研究提出带自监督的元学习与大语言模型上下文学习两种方法，在斯瓦希里语等任务上验证了元训练优于标准微调，且精心设计的提示可实现跨语言上下文学习。
摘要：
及时识别对于抑郁症等精神疾病的高效处置至关重要。然而，当前研究未能充分解决利用斯瓦希里语等低资源非洲语言的社交媒体数据预测心理健康状况的问题。本研究提出两种不同方法，分别利用模型无关元学习与大语言模型（LLM）来填补这一空白。研究在三个被译为低资源语言的数据集上开展实验，应用于四项心理健康任务，包括压力预测、抑郁症预测、抑郁症严重程度预测以及自杀意念预测。我们首先应用带有自监督的元学习模型，该模型可优化模型初始化，以实现快速适配与跨语言迁移。结果表明，我们的元训练模型性能显著优于标准微调方法，其宏F1分数相较基线微调在XLM-R和mBERT上分别高出18%和0.8%。与此同时，我们利用大语言模型的上下文学习能力，通过分析不同的跨语言提示方法，评估其在斯瓦希里语心理健康预测任务上的准确率。我们的分析显示，斯瓦希里语提示的表现优于跨语言提示，但不及英语提示。我们的研究结果表明，通过精心设计带有示例和指令的提示模板，可通过跨语言迁移实现上下文学习。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
