Summary of Adapting Mental Health Prediction Tasks For Cross-lingual Learning Via Meta-training and In-context Learning with Large Language Model, by Zita Lifelo et al.
Adapting Mental Health Prediction Tasks for Cross-lingual Learning via Meta-Training and In-context Learning with Large Language Model
by Zita Lifelo, Huansheng Ning, Sahraoui Dhelim
First submitted to arxiv on: 13 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Computers and Society (cs.CY); Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper tackles the crucial task of detecting mental health conditions, specifically depression, from social media data in low-resource African languages like Swahili. The authors propose two novel approaches to address this gap: model-agnostic meta-learning and leveraging large language models (LLMs). Experiments are conducted on three datasets translated into Swahili, applied to four mental health tasks (stress, depression, depression severity, and suicidal ideation prediction). A meta-learning model with self-supervision is used for rapid adaptation and cross-lingual transfer, yielding improved performance compared to standard fine-tuning methods. The results show that the meta-trained model outperforms baseline fine-tuning in macro F1 score by 18% and 0.8% using XLM-R and mBERT respectively. Additionally, the paper explores LLMs’ in-context learning capabilities for Swahili mental health prediction tasks, demonstrating that carefully crafted prompt templates with examples and instructions can achieve cross-lingual transfer. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research helps identify mental health conditions like depression by analyzing social media posts in African languages. Currently, there isn’t enough work done on this topic in these languages. The authors came up with two new ways to address this issue: using a special learning method and relying on large language models. They tested their ideas on three datasets translated into Swahili, focusing on four mental health tasks (stress, depression, depression severity, and suicidal ideation prediction). Their results show that their approach works better than the usual way of fine-tuning models. The authors also looked at how well large language models can predict mental health conditions in Swahili by using specific prompts. |
Keywords
* Artificial intelligence * F1 score * Fine tuning * Meta learning * Prompt
