Abstract
The remarkable capabilities of large language models have been accompanied by a persistent drawback: the generation of false and unsubstantiated claims commonly known as "hallucinations". To combat this issue, recent research has introduced approaches that involve editing and attributing the outputs of language models, particularly through prompt-based editing. However, the inference cost and speed of using large language models for editing currently bottleneck prompt-based methods. These bottlenecks motivate the training of compact editors, which is challenging due to the scarcity of training data for this purpose. To overcome these challenges, we exploit the power of large language models to introduce corruptions (i.e., noise) into text and subsequently fine-tune compact editors to denoise the corruptions by incorporating relevant evidence. Our methodology is entirely unsupervised and provides us with faux hallucinations for training in any domain. Our Petite Unsupervised Research and Revision model, PURR, not only improves attribution over existing editing methods based on fine-tuning and prompting, but also achieves faster execution times by orders of magnitude.
Abstract (translated)
大型语言模型的卓越能力伴随着一个持久的缺点是生成虚假且缺乏证据的支持声称,这种声称通常被称为“幻觉”。为了解决这个问题,最近的研究引入了涉及编辑和 attributed 语言模型输出的方法,特别是基于提示的编辑。然而,使用大型语言模型进行编辑的推断成本和速度目前的瓶颈是基于提示的方法。这些瓶颈激励了紧凑编辑的训练,但由于训练数据匮乏,这是具有挑战性的。为了克服这些挑战,我们利用大型语言模型的力量将错误(即噪声)引入文本,然后通过集成相关证据微调紧凑编辑,以消除错误。我们的方法论是完全 unsupervised 的,为我们在任何领域训练中的虚假幻觉提供了伪现实。我们的小型 unsupervised 研究和修订模型 purR 不仅基于 fine-tuning 和提示改进了现有的编辑方法,而且通过数倍数的速度加快了执行时间。
URL
https://arxiv.org/abs/2305.14908