Summary of Applying Llm and Topic Modelling in Psychotherapeutic Contexts, by Alexander Vanin et al.
Applying LLM and Topic Modelling in Psychotherapeutic Contexts
by Alexander Vanin, Vadim Bolshev, Anastasia Panfilova
First submitted to arxiv on: 23 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- 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 The paper explores the application of BERTopic, a machine learning-based topic modeling tool, to analyze therapist remarks in a psychotherapeutic setting. The study focuses on identifying and describing topics that consistently emerge across different groups of therapists (classical and modern) using BERTopic’s algorithm, which involves creating a vector space from a corpus of therapist remarks, reducing its dimensionality, clustering the space, and creating topic representations. Expert assessment and manual optimization were also applied to validate the findings. The study highlights the most common and stable topics in therapists’ speech, offering insights into language patterns in therapy. This work contributes to machine learning in psychotherapy by demonstrating the potential of automated methods to improve therapeutic practice and training. The paper emphasizes the value of topic modeling as a tool for understanding therapeutic dialogue, suggesting new opportunities for improving effectiveness and clinical supervision. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The study looks at how big language models can help us understand what therapists say during sessions. Researchers used a tool called BERTopic to analyze remarks from two groups of therapists (old-school and modern) and found common topics that appear across both styles. They took the therapist’s words, turned them into numbers, grouped similar ones together, and then picked out the most important themes. This helps us see how language patterns develop and stay the same even when therapists change their approach. The study shows that computer programs can help make therapy better by giving insights to improve practice and training. |
Keywords
» Artificial intelligence » Clustering » Machine learning » Optimization » Vector space
