Summary of A Dual-prompting For Interpretable Mental Health Language Models, by Hyolim Jeon et al.
A Dual-Prompting for Interpretable Mental Health Language Models
by Hyolim Jeon, Dongje Yoo, Daeun Lee, Sejung Son, Seungbae Kim, Jinyoung Han
First submitted to arxiv on: 20 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI)
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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 aims to improve the interpretability of Large Language Models (LLMs) in mental health analysis by identifying suicidality through linguistic content. The authors propose a dual-prompting approach that leverages domain-specific information and LLMs to extract evidence of suicidality from text data. Specifically, they use an LLM-based consistency evaluator for summarizing evidence and a suicide dictionary with a mental health-specific LLM for extracting knowledge-aware evidence. The experiments demonstrate the effectiveness of combining domain-specific information, revealing performance improvements that can aid clinicians in assessing mental state progression. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper tries to make computers better at understanding human language when it comes to mental health. They want to help doctors figure out if someone is having suicidal thoughts by analyzing what they write or say. To do this, they came up with a new way of using computer models that learn from big datasets. This approach combines two things: knowing what’s important in the context of mental health and using computers to find patterns in language. They tested their idea and found that it works really well. |
Keywords
» Artificial intelligence » Prompting
