Summary of Compass: Computational Mapping Of Patient-therapist Alliance Strategies with Language Modeling, by Baihan Lin et al.
COMPASS: Computational Mapping of Patient-Therapist Alliance Strategies with Language Modeling
by Baihan Lin, Djallel Bouneffouf, Yulia Landa, Rachel Jespersen, Cheryl Corcoran, Guillermo Cecchi
First submitted to arxiv on: 22 Feb 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Human-Computer Interaction (cs.HC); Machine Learning (cs.LG); Neurons and Cognition (q-bio.NC)
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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 COMPASS framework is a novel approach to directly infer the therapeutic working alliance from natural language used in psychotherapy sessions. It utilizes advanced large language models (LLMs) to analyze transcripts of sessions, comparing them with distributed representations of statements in the Working Alliance Inventory. The method demonstrates effectiveness in mapping patient-therapist alignment trajectories and offering interpretability for clinical psychiatry. By combining deep learning-based topic modeling techniques with generative language models, COMPASS analyzes topical characteristics of different psychiatric conditions and their evolution at a turn-level resolution. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Psychotherapy is a powerful way to help people feel better when they’re struggling with mental health issues. A strong connection between the therapist and patient is important for treatment success. Researchers have developed a new way to measure this connection, called COMPASS. It uses special computer programs that can understand natural language to analyze recordings of therapy sessions. By studying these sessions, COMPASS helps therapists understand how well they’re working with their patients and provides insights to improve the therapy process. |
Keywords
* Artificial intelligence * Alignment * Deep learning
