Current transformer language models (LM) are large-scale models with billions of parameters. They have been shown to provide high performances on a variety of tasks but are also prone to shortcut learning and bias. Addressing such incorrect model behavior via parameter adjustments is very costly. This is particularly problematic for updating dynamic concepts, such as moral values, which vary culturally or interpersonally. In this work, we question the current common practice of storing all information in the model parameters and propose the Revision Transformer (RiT) to facilitate easy model updating. The specific combination of a large-scale pre-trained LM that inherently but also diffusely encodes world knowledge with a clear-structured revision engine makes it possible to update the model's knowledge with little effort and the help of user interaction. We exemplify RiT on a moral dataset and simulate user feedback demonstrating strong performance in model revision even with small data. This way, users can easily design a model regarding their preferences, paving the way for more transparent AI models.

翻译：当前Transformer语言模型（LM）是拥有数十亿参数的大规模模型。这类模型在多种任务中展现出卓越性能，但同时也容易产生捷径学习和偏见问题。通过参数调整来修正此类错误模型行为成本极高。这对更新诸如道德价值观等具有文化差异或人际差异的动态概念尤为困难。本研究质疑了当前将所有信息存储于模型参数的普遍做法，提出修正Transformer（RiT）以实现便捷的模型更新。该方案将固有但分散编码世界知识的大规模预训练语言模型与结构清晰的修正引擎相结合，使得通过少量努力和用户交互即可更新模型知识。我们通过道德数据集示例验证RiT，并模拟用户反馈，证明即使使用少量数据，该模型在修正任务中仍表现优异。由此，用户可根据偏好轻松设计模型，为更具透明度的AI模型铺平道路。