Summary of Severity Prediction in Mental Health: Llm-based Creation, Analysis, Evaluation Of a Novel Multilingual Dataset, by Konstantinos Skianis et al.
Severity Prediction in Mental Health: LLM-based Creation, Analysis, Evaluation of a Novel Multilingual Dataset
by Konstantinos Skianis, John Pavlopoulos, A. Seza Doğruöz
First submitted to arxiv on: 25 Sep 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: 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 focuses on the effectiveness of Large Language Models (LLMs) in non-English mental health support applications. A novel multilingual adaptation of widely-used mental health datasets is presented, translated from English into six languages: Greek, Turkish, French, Portuguese, German, and Finnish. The dataset enables a comprehensive evaluation of LLM performance in detecting mental health conditions and assessing their severity across multiple languages. The study uses GPT and Llama to observe considerable variability in performance across languages, despite being evaluated on the same translated dataset. This inconsistency highlights the complexities inherent in multilingual mental health support, where language-specific nuances and mental health data coverage can affect the accuracy of models. The paper also emphasizes the risks of relying exclusively on LLMs in medical settings, such as potential misdiagnoses. Additionally, the proposed approach offers significant cost savings for multilingual tasks, presenting a major advantage for broad-scale implementation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research looks at how well computers can help people with mental health issues when speaking different languages. Right now, there’s not much known about this topic. The researchers created a big database of information about mental health in six different languages: Greek, Turkish, French, Portuguese, German, and Finnish. They used this database to test how good computer models are at recognizing mental health problems and understanding how severe they are in each language. They found that the computer models didn’t perform well across all languages, which is important because language can make a big difference in how people talk about their mental health. The researchers also want people to know that relying too much on computers for mental health help might not be a good idea. |
Keywords
» Artificial intelligence » Gpt » Llama
