Summary of Multi-level Feedback Generation with Large Language Models For Empowering Novice Peer Counselors, by Alicja Chaszczewicz et al.
Multi-Level Feedback Generation with Large Language Models for Empowering Novice Peer Counselors
by Alicja Chaszczewicz, Raj Sanjay Shah, Ryan Louie, Bruce A Arnow, Robert Kraut, Diyi Yang
First submitted to arxiv on: 21 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Human-Computer Interaction (cs.HC); Machine Learning (cs.LG)
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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 This paper presents a novel approach to empowering peer counselors by leveraging large language models to provide contextualized and multi-level feedback. The goal is to overcome the limitations of existing mechanisms, which rely heavily on human supervision, and enable peer counselors to support individuals with mental health issues at scale. To achieve this, the authors co-design a multi-level feedback taxonomy with senior psychotherapy supervisors and construct a publicly available dataset with comprehensive feedback annotations of 400 emotional support conversations. The paper also designs a self-improvement method on top of large language models to enhance the automatic generation of feedback. Through qualitative and quantitative evaluation with domain experts, the authors demonstrate that their method minimizes the risk of potentially harmful and low-quality feedback generation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps us understand how computers can help train peer counselors who talk to people about their mental health. Right now, these peer counselors don’t always get the detailed feedback they need from experienced mentors. This makes it hard for them to support all the people who need their help. The authors of this paper want to change that by using special computer programs called large language models to give peer counselors better feedback. They worked with experts in psychology to create a list of different levels of feedback and collected examples of conversations where someone might be feeling emotional support. Then, they used computers to improve how well the feedback is written. The authors show that their method helps make sure the computer-generated feedback is good quality and doesn’t cause any harm. |
