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.

本文介绍了序列可能性校准（SLiC）的方法，在序列生成过程中较大束搜索大小时生成质量会下降，通过使用 SLiC 可以不用启用启发式方法，从而显著提高候选解的生成质量。与传统的 MLE 模型相比，我们在许多生成任务中都获得了更好的结果。