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.

翻译：许多促进心理健康的认知方法，例如识别和重构无益思维，在过去几十年已获得大量实证支持，但仍未在自助形式中得到广泛采用。其障碍之一在于缺乏足够具体且多样化的专用练习材料。本研究探讨当前语言模型能否用于生成几乎无限量的、符合特定情境的标准无益思维模式练习材料，以及提出恰当的正向重构建议。我们提出了PATTERNREFRAME这一新型数据集，包含约1万个基于特定人物背景、含有无益思维模式的思维示例，并配有约2.7万个正向重构方案。通过利用该数据集训练和/或评估现有模型，我们证明现有模型已能成为强大的工具，在几乎无需额外模型训练的情况下，帮助生成大量定制化练习材料与假设。