PURR: Efficiently Editing Language Model Hallucinations by Denoising
  Language Model Corruptions
Anthony Chen Panupong Pasupat Sameer Singh Hongrae Lee Kelvin Guu
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.