  Conditional language models are predominantly trained with maximum likelihood
estimation (MLE), giving probability mass to sparsely observed target
sequences. While MLE trained models assign high probability to plausible
sequences given the context, the model probabilities often do not accurately
rank-order generated sequences by quality. This has been empirically observed
in beam search decoding as output quality degrading with large beam sizes, and
decoding strategies benefiting from heuristics such as length normalization and
repetition-blocking. In this work, we introduce sequence likelihood calibration
(SLiC) where the likelihood of model generated sequences are calibrated to
better align with reference sequences in the model's latent space. With SLiC,
decoding heuristics become unnecessary and decoding candidates' quality
significantly improves regardless of the decoding method. Furthermore, SLiC
shows no sign of diminishing returns with model scale, and presents alternative
ways to improve quality with limited training and inference budgets. With SLiC,
we exceed or match SOTA results on a wide range of generation tasks spanning
abstractive summarization, question generation, abstractive question answering
and data-to-text generation, even with modest-sized models.
