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.

本文探讨现有的语言模型是否可以产生大量的实践材料来帮助生成特定上下文中标准的无益思维模式，并提出积极的重新构建建议。通过使用一个包含大约10k个例子的新数据集来训练和/或评估当前模型，我们发现现有模型已经可以帮助生成丰富的定制实践材料和假设，无需或最少额外模型训练。

训练生成、识别和重构无益思维模式的模型