Summary of Leveraging Audio and Text Modalities in Mental Health: a Study Of Llms Performance, by Abdelrahman A. Ali et al.
Leveraging Audio and Text Modalities in Mental Health: A Study of LLMs Performance
by Abdelrahman A. Ali, Aya E. Fouda, Radwa J. Hanafy, Mohammed E. Fouda
First submitted to arxiv on: 9 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Sound (cs.SD); Audio and Speech Processing (eess.AS)
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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 The study explores the potential of Large Language Models (LLMs) in multimodal mental health diagnostics for detecting depression and Post Traumatic Stress Disorder through text and audio modalities. The researchers compare text and audio inputs using the E-DAIC dataset, finding that combining both modalities can enhance diagnostic accuracy. The Gemini 1.5 Pro model achieves high scores in binary depression classification with an F1 score of 0.67 and Balanced Accuracy (BA) of 77.4%. The results demonstrate the effectiveness of integrating modalities without requiring task-specific fine-tuning, showcasing the robustness of LLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study shows that large language models can be used to help diagnose depression and post-traumatic stress disorder. Researchers tested these models using text and audio recordings, and found that combining both types of data made them more accurate. This is important because it could help people get the treatment they need sooner. |
Keywords
» Artificial intelligence » Classification » F1 score » Fine tuning » Gemini
