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Found 3 papers, 2 papers with code
A Dual-Prompting for Interpretable Mental Health Language Models
Despite the increasing demand for AI-based mental health monitoring tools, their practical utility for clinicians is limited by the lack of interpretability. The CLPsych 2024 Shared Task (Chim et al., 2024) aims to enhance the interpretability of Large Language Models (LLMs), particularly in mental health analysis, by providing evidence of suicidality through linguistic content.
Learning Co-Speech Gesture for Multimodal Aphasia Type Detection
By learning the correlation between the speech and gesture modalities for each aphasia type, our model can generate textual representations sensitive to gesture information, leading to accurate aphasia type detection.
Towards Suicide Prevention from Bipolar Disorder with Temporal Symptom-Aware Multitask Learning
Bipolar disorder (BD) is closely associated with an increased risk of suicide.
