训练模型以生成、识别和重构无助思维

摘要

许多认知方法来提升幸福感，比如识别和重构无益思维，在过去几十年中得到了相当多的实证支持，但在自助格式中仍然缺乏真正广泛的采纳。导致这种采纳困难的一个障碍是缺乏足够具体和多样化的专门练习材料。本研究探讨了当前语言模型是否能够被利用来产生大量实践材料，展示标准无益思维模式匹配特定给定背景，并生成适当的积极重构建议。我们提出了PATTERNREFRAME，一个包含约10k个思维示例的新颖数据集，这些示例包含无益思维模式，根据给定人物条件，伴随着约27k个积极重构。通过使用这个数据集来训练和/或评估当前模型，我们展示了现有模型已经可以成为强大的工具，帮助生成大量量身定制的练习材料和假设，而无需或只需最少额外的模型训练。

English

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.

训练模型以生成、识别和重构无助思维

Training Models to Generate, Recognize, and Reframe Unhelpful Thoughts

摘要

Support