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Related Experiment Video

Updated: May 31, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Large language model empowered explainable and interpretable mental health analysis.

Supriya Bajpai¹, Gargi Mishra², Rachna Jain³

¹Indian Institute of Technology Bombay, IITB-Monash Research Academy, Mumbai, 400076, India.

Scientific Reports

|May 29, 2026

Summary

This summary is machine-generated.

InsightDep is a new AI model that detects depression on social media with explanations. It uses advanced language models to make results understandable for mental health professionals.

Keywords:

Explainable artificial intelligence Generative artificial intelligence Interpretable artificial intelligence Large language model Mental health analysis

Related Experiment Videos

Last Updated: May 31, 2026

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Augmenting Large Language Models via Vector Embeddings to Improve Domain-Specific Responsiveness

Published on: December 6, 2024

Area of Science:

Artificial Intelligence
Computational Linguistics
Mental Health Technology

Background:

Growing concern over mental health expression on social media.
Limitations of traditional depression detection methods in providing clear explanations.
Need for interpretable AI in mental health analysis.

Purpose of the Study:

Introduce InsightDep, an explainable AI model for detecting depression on social media.
Enhance interpretability of AI-driven mental health analysis using Large Language Models (LLMs).
Facilitate practical application in therapeutic assessments by medical experts.

Main Methods:

Utilized a BERT model variant adapted for Twitter data analysis.
Implemented masked attention techniques for classification and explainability.
Integrated LLMs to translate complex model explanations into human-understandable formats.

Main Results:

InsightDep achieved high performance on Twitter and Reddit datasets (macro-F1/accuracy: 0.599/0.671 and 0.994/0.994).
Outperformed existing state-of-the-art methods like BERTweet, TwHIN-BERT, and DepRoBERTa.
Demonstrated effective translation of technical results into interpretable insights.

Conclusions:

InsightDep offers a novel, explainable approach to social media depression detection.
The methodology supports the development of morally aware digital mental health channels.
The system is designed for practical use by licensed medical professionals in therapeutic settings.