Summary of Ai-enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models For Extracting Cognitive Pathways From Social Media Texts, by Meng Jiang et al.
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
by Meng Jiang, Yi Jing Yu, Qing Zhao, Jianqiang Li, Changwei Song, Hongzhi Qi, Wei Zhai, Dan Luo, Xiaoqin Wang, Guanghui Fu, Bing Xiang Yang
First submitted to arxiv on: 17 Apr 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Machine Learning (cs.LG)
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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 presents a novel approach for analyzing cognitive pathways on social media, enabling psychotherapists to conduct effective online interventions. Researchers gathered data from social media platforms, annotating it based on a cognitive theoretical framework. They categorized the task of extracting cognitive pathways as a hierarchical text classification problem and developed a deep learning method achieving a micro-F1 score of 62.34%. The study also evaluated the performance of large language models (LLMs), including GPT-4, which attained a Rouge-1 score of 54.92 and a Rouge-2 score of 30.86 in text summarization tasks. However, LLMs may suffer from hallucination issues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how people express negative thoughts on social media, often showing signs of mental illnesses like suicidal behaviors. They want to help psychotherapists understand these online behaviors so they can provide better care. To do this, the researchers collected data from social media and organized it into categories that fit with cognitive behavioral therapy. They then tested different computer models to see which ones could best identify and summarize these online behaviors. The results show that a deep learning model did well on one task, but large language models were better at summarizing text while still having some limitations. |
Keywords
» Artificial intelligence » Deep learning » F1 score » Gpt » Hallucination » Rouge » Summarization » Text classification
