PEER: A collaborative language model

https://ar5iv.labs.arxiv.org/html/2208.11663

Text from current language models can be useful as a rough draft, but that leaves the polishing to human writers. A language model learned how to generate and respond to editorial directions. What’s new: Timo Schick and colleagues at Meta proposed Plan, Edit, Explain, and Repeat (PEER), a text generator designed to collaborate with human writers.

Key insight: Data that demonstrates the motivations, execution, and results of editing is hard to come by. Wikipedia, in which every article includes a history of edits as well as comments on them, comes close, but an editor trained solely on Wikipedia would be limited to encyclopedia-style text. However, a model trained on Wikipedia to undo revisions can synthesize a supplemental dataset of unrevised and revised examples. Applying the undo function to varied text can generate synthetic “unedited” drafts for training the editor.

How it works: PEER comprises four T5 large language models: PEER-Edit (which executed revisions), PEER-Undo (which undid revisions), PEER-Explain (which explained revisions), and PEER-Document (which generated synthetic primary-source documents as a basis for revisions). The authors trained them on Wikipedia, 6.9 million examples that include texts before and after a revision, a revision plan (a directive to revise the text, such as “add information about the scandal”), an explanation (a reason for the revision, which may duplicate the revision plan), and cited documents (primary sources on which the text is based).

Why it matters: Training text generators to provide explanations for their decisions and citations for the facts they use may lead to more interpretable models.

We’re thinking: The raw output of generative models is fun and exciting, but imagine their potential as collaborators with creative people!