Boosting coherence of language models

Naturality of long-term information structure – coherence – remains a challenge in language generation. Large language models have insufficiently learned such structure, as their longform generations differ from natural text in measures of coherence. To alleviate this divergence, we propose coherence boosting, an inference procedure that increases the effect of distant context on next-token prediction. We show the benefits of coherence boosting with pretrained models by distributional analyses of generated ordinary text and dialog responses. We also find that coherence boosting with state-of-the-art models for various zeroshot NLP tasks yields performance gains with no additional training.