Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors
Fuente:
arXiv
Salvato in:
| Autori principali: | , , , , , |
|---|---|
| Natura: | Preprint |
| Pubblicazione: |
2024
|
| Soggetti: | |
| Accesso online: | |
| Tags: |
Aggiungi Tag
Nessun Tag, puoi essere il primo ad aggiungerne!!
|
| _version_ | 1866910381337739264 |
|---|---|
| author | Chaszczewicz, Alicja Shah, Raj Sanjay Louie, Ryan Arnow, Bruce A Kraut, Robert Yang, Diyi |
| author_facet | Chaszczewicz, Alicja Shah, Raj Sanjay Louie, Ryan Arnow, Bruce A Kraut, Robert Yang, Diyi |
| contents | Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on human supervision. Peer counselors often lack mechanisms to receive detailed feedback from experienced mentors, making it difficult for them to support the large number of people with mental health issues who use peer counseling. Our work aims to leverage large language models to provide contextualized and multi-level feedback to empower peer counselors, especially novices, at scale. To achieve this, we co-design with a group of senior psychotherapy supervisors to develop a multi-level feedback taxonomy, and then construct a publicly available dataset with comprehensive feedback annotations of 400 emotional support conversations. We further design a self-improvement method on top of large language models to enhance the automatic generation of feedback. Via qualitative and quantitative evaluation with domain experts, we demonstrate that our method minimizes the risk of potentially harmful and low-quality feedback generation which is desirable in such high-stakes scenarios. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2403_15482 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors Chaszczewicz, Alicja Shah, Raj Sanjay Louie, Ryan Arnow, Bruce A Kraut, Robert Yang, Diyi Computation and Language Human-Computer Interaction Machine Learning Realistic practice and tailored feedback are key processes for training peer counselors with clinical skills. However, existing mechanisms of providing feedback largely rely on human supervision. Peer counselors often lack mechanisms to receive detailed feedback from experienced mentors, making it difficult for them to support the large number of people with mental health issues who use peer counseling. Our work aims to leverage large language models to provide contextualized and multi-level feedback to empower peer counselors, especially novices, at scale. To achieve this, we co-design with a group of senior psychotherapy supervisors to develop a multi-level feedback taxonomy, and then construct a publicly available dataset with comprehensive feedback annotations of 400 emotional support conversations. We further design a self-improvement method on top of large language models to enhance the automatic generation of feedback. Via qualitative and quantitative evaluation with domain experts, we demonstrate that our method minimizes the risk of potentially harmful and low-quality feedback generation which is desirable in such high-stakes scenarios. |
| title | Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors |
| topic | Computation and Language Human-Computer Interaction Machine Learning |
| url | https://arxiv.org/abs/2403.15482 |