@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.",
}

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.
Sharma, Ashish Rushton, Kevin Lin, Inna Wadden, David Lucas, Khendra Miner, Adam Nguyen, Theresa Althoff, Tim
Cognitive Reframing of Negative Thoughts through Human-Language Model Interaction