Summary of Evaluating the Effectiveness Of the Foundational Models For Q&a Classification in Mental Health Care, by Hassan Alhuzali and Ashwag Alasmari
Evaluating the Effectiveness of the Foundational Models for Q&A Classification in Mental Health care
by Hassan Alhuzali, Ashwag Alasmari
First submitted to arxiv on: 23 Jun 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
GrooveSquid.com Paper Summaries
GrooveSquid.com’s goal is to make artificial intelligence research accessible by summarizing AI papers in simpler terms. Each summary below covers the same AI paper, written at different levels of difficulty. The medium difficulty and low difficulty versions are original summaries written by GrooveSquid.com, while the high difficulty version is the paper’s original abstract. Feel free to learn from the version that suits you best!
| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The study evaluates the effectiveness of pre-trained language models (PLMs) for classifying questions and answers related to mental health care in Arabic. Four learning approaches are tested: traditional feature extraction, PLM as feature extractor, fine-tuning PLMs, and prompting large language models (GPT-3.5 and GPT-4) in zero-shot and few-shot learning settings. The results show that PLMs outperform traditional feature extractors, with MARBERT achieving the highest performance for question classification and answer classification. Fine-tuning is found to be beneficial for enhancing PLM performance, while varying data size plays a crucial role in achieving high performance. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary PLMs can help mental health support by providing accessible resources. This study explores if these models work well for Arabic questions and answers about mental health. The researchers tried different ways of using the models: traditional methods, letting the models learn from data, fine-tuning the models to get better results, and prompting them to make predictions. They found that the models did a great job when fine-tuned or prompted, especially with GPT-3.5. This shows that PLMs can be useful for mental health support in Arabic. |
Keywords
» Artificial intelligence » Classification » Feature extraction » Few shot » Fine tuning » Gpt » Prompting » Zero shot
