@inproceedings{oneil-etal-2023-automatic,
title = "Automatic Reflection Generation for Peer-to-Peer Counseling",
author = "O{'}neil, Emma and
Sedoc, Jo{\~a}o and
Yang, Diyi and
Zhu, Haiyi and
Ungar, Lyle",
editor = "Gehrmann, Sebastian and
Wang, Alex and
Sedoc, Jo{\~a}o and
Clark, Elizabeth and
Dhole, Kaustubh and
Chandu, Khyathi Raghavi and
Santus, Enrico and
Sedghamiz, Hooman",
booktitle = "Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.gem-1.6",
pages = "62--75",
abstract = "Online peer counseling platforms enable conversations between millions of people seeking and offering mental health support. Among counseling skills, reflective listening, i.e., capturing and returning to the client something the client has said, is important for positive therapeutic outcomes. We introduce a reflection generation system for online mental health support conversations leveraging GPT-3, a large language model. We compare few-shot learning against fine-tuning and assess the impact of the quality of training examples as measured by fluency, reflection resemblance, and overall preference. Fine-tuned GPT-3 generates responses that human evaluators rate as comparable in reflection quality to responses used for tuning. Models based on high-quality responses generate substantially better reflections than ones tuned on actual responses from a large online counseling service{--}and better reflections than the actual counselor responses. These results suggest the care needed in selecting examples for tuning generative models.",
}
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<abstract>Online peer counseling platforms enable conversations between millions of people seeking and offering mental health support. Among counseling skills, reflective listening, i.e., capturing and returning to the client something the client has said, is important for positive therapeutic outcomes. We introduce a reflection generation system for online mental health support conversations leveraging GPT-3, a large language model. We compare few-shot learning against fine-tuning and assess the impact of the quality of training examples as measured by fluency, reflection resemblance, and overall preference. Fine-tuned GPT-3 generates responses that human evaluators rate as comparable in reflection quality to responses used for tuning. Models based on high-quality responses generate substantially better reflections than ones tuned on actual responses from a large online counseling service–and better reflections than the actual counselor responses. These results suggest the care needed in selecting examples for tuning generative models.</abstract>
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%0 Conference Proceedings
%T Automatic Reflection Generation for Peer-to-Peer Counseling
%A O’neil, Emma
%A Sedoc, João
%A Yang, Diyi
%A Zhu, Haiyi
%A Ungar, Lyle
%Y Gehrmann, Sebastian
%Y Wang, Alex
%Y Sedoc, João
%Y Clark, Elizabeth
%Y Dhole, Kaustubh
%Y Chandu, Khyathi Raghavi
%Y Santus, Enrico
%Y Sedghamiz, Hooman
%S Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM)
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F oneil-etal-2023-automatic
%X Online peer counseling platforms enable conversations between millions of people seeking and offering mental health support. Among counseling skills, reflective listening, i.e., capturing and returning to the client something the client has said, is important for positive therapeutic outcomes. We introduce a reflection generation system for online mental health support conversations leveraging GPT-3, a large language model. We compare few-shot learning against fine-tuning and assess the impact of the quality of training examples as measured by fluency, reflection resemblance, and overall preference. Fine-tuned GPT-3 generates responses that human evaluators rate as comparable in reflection quality to responses used for tuning. Models based on high-quality responses generate substantially better reflections than ones tuned on actual responses from a large online counseling service–and better reflections than the actual counselor responses. These results suggest the care needed in selecting examples for tuning generative models.
%U https://aclanthology.org/2023.gem-1.6
%P 62-75
Markdown (Informal)
[Automatic Reflection Generation for Peer-to-Peer Counseling](https://aclanthology.org/2023.gem-1.6) (O’neil et al., GEM-WS 2023)
ACL
- Emma O’neil, João Sedoc, Diyi Yang, Haiyi Zhu, and Lyle Ungar. 2023. Automatic Reflection Generation for Peer-to-Peer Counseling. In Proceedings of the Third Workshop on Natural Language Generation, Evaluation, and Metrics (GEM), pages 62–75, Singapore. Association for Computational Linguistics.