From: NeurIPS.cc/2022/Workshop/DistShift/Program_Chairs
title: Paper Decision
decision: Accept (Poster)
comment: The paper investigates controlled text generation via generate-and-filter under situations in which the training distribution and the "target distribution" are different. The paper demonstrates that an application of the invariance-prompting objective V-REX can reduce toxicity and improve cross-entropy in comparison with ERM. This works even though there is no natural notion of environment in CivilComments dataset, using metadata in place of environments.

Personally I found the notion of target distribution hard to understand except in the setting of completing prompts, but the formulation didn't distinguish between the prompts and the generated text. Future versions of the paper might do a bit more to tie together the rejection sampling idea with the (fairly standard) application of invariance-promoting regularization.