Summary of Response Generation For Cognitive Behavioral Therapy with Large Language Models: Comparative Study with Socratic Questioning, by Kenta Izumi et al.
Response Generation for Cognitive Behavioral Therapy with Large Language Models: Comparative Study with Socratic Questioning
by Kenta Izumi, Hiroki Tanaka, Kazuhiro Shidara, Hiroyoshi Adachi, Daisuke Kanayama, Takashi Kudo, Satoshi Nakamura
First submitted to arxiv on: 29 Jan 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 In this study, researchers investigate the potential of large language models (LLMs) to enhance mental health apps by generating contextually relevant utterances. They design dialogue modules based on cognitive behavioral therapy (CBT) scenarios and train two types of LLMs: a Transformer-based model further trained with an empathetic counseling dataset, and GPT-4, a state-of-the-art LLM created by OpenAI. The study compares the performance of systems using LLM-generated responses versus those without, evaluating subjective outcomes such as mood change, cognitive change, and dialogue quality (e.g., empathy). The results suggest that GPT-4 shows significant improvements in these metrics, indicating its high counseling ability. However, the study also finds that even with a human-counseling dataset-trained model, it does not necessarily yield better outcomes compared to scenario-based dialogues. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This research explores how language models can improve mental health apps by generating helpful conversations. The scientists train special kinds of language models to understand and respond like counselors. They test these models against each other and find that one model, called GPT-4, does a great job making people feel better and understanding them more empathetically. While this is good news, it also raises important questions about using language models in real-life mental health care. |
