Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts (2023)

Abstract: Many cognitive approaches to well-being, such as recognizing and reframing unhelpful thoughts, have received considerable empirical support over the past decades, yet still lack truly widespread adoption in self-help format. A barrier to that adoption is a lack of adequately specific and diverse dedicated practice material. This work examines whether current language models can be leveraged to both produce a virtually unlimited quantity of practice material illustrating standard unhelpful thought patterns matching specific given contexts, and generate suitable positive reframing proposals. We propose PATTERNREFRAME, a novel dataset of about 10k examples of thoughts containing unhelpful thought patterns conditioned on a given persona, accompanied by about 27k positive reframes. By using this dataset to train and/or evaluate current models, we show that existing models can already be powerful tools to help generate an abundance of tailored practice material and hypotheses, with no or minimal additional model training required.

## introduction cognitive behavioral therapy (cbt) @xcite is one of the most robustly validated approaches in psychology @xcite . a core pillar of cbt consists in identifying and reframing unhelpful ways of thinking. low-intensity cbt interventions have shown promise in self-help formats @xcite ), yet a lack of sufficient practice material suited to people's specific circumstances is a barrier to adoption @xcite . through prompting, control tokens, or adequate conditioning, modern language models can guide generation of language towards desired outcomes, such as conforming to a given persona @xcite , style @xcite , or level of confidence @xcite . this makes them a potentially powerful practice aid for learning cog-nitive reframing techniques. a major barrier is the lack of publicly available data. most existing work in natural language processing (nlp) for cbt focuses on interactions between patients and mental health professionals, which are not publicly available @xcite @xcite . @xcite released the first public dataset for reframing tweets marked with a hashtag indicating stress, using known reframing techniques, but it does not specifically look at the categories of unhelpful thinking used in cbt, and uses existing tweets rather than allowing the generation of examples suited to a particular situation. in this work, we propose foot_0 a novel dataset, pat-ternreframe, consisting in ∼10k crowdsourced examples of thoughts containing ten classical types of unhelpful thought patterns @xcite , conditioned on personas, matched with crowdsourced proposals of reframing that do not exhibit the patterns. we introduce two controllable text-to-text generation tasks on the dataset: (1) generating and (2) reframing unhelpful thoughts, given a persona and pattern as the context. we also define a classification task to identify the unhelpful thought pattern, given a persona and a thought. we train and evaluate different fine-tuned and few-shot approaches for the tasks, and show that these approaches perform reasonably well on the tasks. depression @xcite @xcite , schizophrenia @xcite @xcite , and post-traumatic stress disorder @xcite . most of these works annotate social media posts to create datasets for the task, and then train and evaluate different classification models. @xcite and @xcite created datasets for identifying unhelpful thoughts by annotating patient-therapist interactions and finetuned different pretrained models for the task. however, these datasets are not publicly available. the closest work to ours is that of @xcite , which introduces a reframing task, releases a parallel corpus of reframed sentences, and uses controllable text generation models to reframe social media content from twitter that was marked as expressing stress. however, the source social media material is not conditioned on personas, or focused on the classical unhelpful thought patterns from cbt. our work introduces conditioning on personas and classical unhelpful thought patterns, and extends the reframing task to identifying and generating thoughts matching a given persona and unhelpful pattern. ## identifying and reframing unhelpful thoughts we use the ten categories of unhelpful thought patterns described in lay terms in a widely used cbt self-help book used for bibliotherapy @xcite . ## conclusion in this work, we introduced a novel dataset, pat-ternreframe, which contains (1) about 10k statements exhibiting unhelpful thought patterns, conditioned on a persona, and (2) multiple rewritten complementary thoughts that do not contain the initial unhelpful pattern, instead reframing the thought in a more constructive way. using this dataset to train or prompt various mod-ern language models, we showed that this range of models can already be a powerful tool to generate, identify, and reframe unhelpful thoughts, conditioned on a persona. by releasing our dataset 9 , we hope to help practitioners of cbt draw from a richer, more diverse set of examples of unhelpful thought patterns and reframings. this would help address the important limitation of a lack of personalized and specific examples in existing datasets, when teaching cognitive techniques. future work will evaluate whether leveraging models to produce richer training material results in more robust learning and understanding of the types of unhelpful thought patterns in humans.this may serve as the basis for future psychological validation studies of the materials and support future studies of low-intensity self-help interventions.