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Preprint (Author's original)arXiv.org - Non-exclusive license to distribute, Open
Preprint
From Conversation to Automation: Leveraging Large Language Models to Analyze Strategies in Problem Solving Therapy
10 Jan 2025
Abstract
Problem-solving therapy (PST) is a structured psychological approach that
helps individuals manage stress and resolve personal issues by guiding them
through problem identification, solution brainstorming, decision-making, and
outcome evaluation. As mental health care increasingly integrates technologies
like chatbots and large language models (LLMs), understanding how PST can be
effectively automated is important. This study leverages anonymized therapy
transcripts to analyze and classify therapeutic interventions using various
LLMs and transformer-based models. Our results show that GPT-4o achieved the
highest accuracy (0.76) in identifying PST strategies, outperforming other
models. Additionally, we introduced a new dimension of communication strategies
that enhances the current PST framework, offering deeper insights into
therapist-client interactions. This research demonstrates the potential of LLMs
to automate complex therapeutic dialogue analysis, providing a scalable,
efficient tool for mental health interventions. Our annotation framework can
enhance the accessibility, effectiveness, and personalization of PST,
supporting therapists in real-time with more precise, targeted interventions.
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Details
- Title
- From Conversation to Automation: Leveraging Large Language Models to Analyze Strategies in Problem Solving Therapy
- Creators
- Elham AghakhaniLu WangKarla T WashingtonGeorge DemirisJina Huh-YooRezvaneh Rezapour
- Resource Type
- Preprint
- Language
- English
- Academic Unit
- Information Science
- Other Identifier
- 991022018790904721