@inproceedings{sharma-etal-2023-cognitive,
title = "Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction",
author = "Sharma, Ashish and
Rushton, Kevin and
Lin, Inna and
Wadden, David and
Lucas, Khendra and
Miner, Adam and
Nguyen, Theresa and
Althoff, Tim",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.acl-long.555",
doi = "10.18653/v1/2023.acl-long.555",
pages = "9977--10000",
abstract = "A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful {``}reframed thought.{''} Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people{'}s access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a {``}high-quality{''} reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts.",
}
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<abstract>A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful “reframed thought.” Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people’s access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a “high-quality” reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts.</abstract>
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%0 Conference Proceedings
%T Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction
%A Sharma, Ashish
%A Rushton, Kevin
%A Lin, Inna
%A Wadden, David
%A Lucas, Khendra
%A Miner, Adam
%A Nguyen, Theresa
%A Althoff, Tim
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F sharma-etal-2023-cognitive
%X A proven therapeutic technique to overcome negative thoughts is to replace them with a more hopeful “reframed thought.” Although therapy can help people practice and learn this Cognitive Reframing of Negative Thoughts, clinician shortages and mental health stigma commonly limit people’s access to therapy. In this paper, we conduct a human-centered study of how language models may assist people in reframing negative thoughts. Based on psychology literature, we define a framework of seven linguistic attributes that can be used to reframe a thought. We develop automated metrics to measure these attributes and validate them with expert judgements from mental health practitioners. We collect a dataset of 600 situations, thoughts and reframes from practitioners and use it to train a retrieval-enhanced in-context learning model that effectively generates reframed thoughts and controls their linguistic attributes. To investigate what constitutes a “high-quality” reframe, we conduct an IRB-approved randomized field study on a large mental health website with over 2,000 participants. Amongst other findings, we show that people prefer highly empathic or specific reframes, as opposed to reframes that are overly positive. Our findings provide key implications for the use of LMs to assist people in overcoming negative thoughts.
%R 10.18653/v1/2023.acl-long.555
%U https://aclanthology.org/2023.acl-long.555
%U https://doi.org/10.18653/v1/2023.acl-long.555
%P 9977-10000
Markdown (Informal)
[Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction](https://aclanthology.org/2023.acl-long.555) (Sharma et al., ACL 2023)
ACL
- Ashish Sharma, Kevin Rushton, Inna Lin, David Wadden, Khendra Lucas, Adam Miner, Theresa Nguyen, and Tim Althoff. 2023. Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), pages 9977–10000, Toronto, Canada. Association for Computational Linguistics.