Language models (LMs)  now excel at many tasks such as question answering, reasoning, and dialog.
However, they sometimes generate unsupported or misleading content.
A user cannot easily determine whether their outputs are trustworthy or not, because most LMs do not have any built-in mechanism forattributionto external evidence.
To enable attribution while still preserving all the powerful advantages of recent generation models, we propose RARR (Retrofit Attribution using Research and Revision) , a system that 1)  automatically finds attribution for the output of any text generation model, and 2)  post-edits the output to fix unsupported content while preserving the original output as much as possible.
When applied to the output of several state-of-the-art LMs on a diverse set of generation tasks, we find that RARR significantly improves attribution while otherwise preserving the original input to a much greater degree than previously explored edit models. Furthermore, the implementation of RARR requires only a handful of training examples, a large language model, and standard web search.111We release open-source implementations of RARR, the evaluation pipeline, and the evaluation sets athttps://github.com/anthonywchen/RARR.