Leveraging Large Language Models for Mental Health Prediction via Online Text Data
By Xuhai Xu, Bingsheng Yao, Yuanzhe Dong, Hong Yu, James A. Hendler, Anind K. Dey, Dakuo Wang. arXiv.org, 2023
305 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Social Psychology
View Paper: https://www.semanticscholar.org/paper/ea284d2045672daf44deffa3f0b7ce154630424c
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Citations in the corpus
- A New Pipeline for Generating Instruction Dataset via RAG and Self Fine-Tuning (2024, methodology): “Additionally, the Mental-LLM proposed by Xu et al. [12] utilizes online text data for the prevention of mental disorders.”
- MAQuA: Multi-outcome Adaptive Question-Asking for Mental Health using Item Response Theory (2026, unknown): “While large language models (LLMs) have shown promising results in zero-shot and few-shot prediction tasks (Ganesan et al., 2024; Hur et al., 2024), finetuned or instruction-tuned models remain generally more reliable and better validated across a range of mental health outcomes (Xu et al., 2024).”