Constrained Sampling from Language Models via Langevin Dynamics in Embedding Spaces. (arXiv:2205.12558v1 [cs.CL])

Large pre-trained language models are well-established for their ability to
generate text seemingly indistinguishable from humans. In this work, we study
the problem of constrained sampling from such language models. That is,
generating text that satisfies user-defined constraints. Typical decoding
strategies which generate samples left-to-right are not always conducive to
imposing such constraints globally. Instead, we propose MuCoLa -- a sampling
procedure that combines the log-likelihood of the language model with arbitrary
differentiable constraints into a single energy function; and …

arxiv dynamics embedding language language models sampling