  Long-range semantic coherence remains a challenge in automatic language
generation and understanding. We demonstrate that large language models have
insufficiently learned the effect of distant words on next-token prediction. We
present coherence boosting, an inference procedure that increases a LM's focus
on a long context. We show the benefits of coherence boosting with pretrained
models by distributional analyses of generated ordinary text and dialog
responses. It is also found that coherence boosting with state-of-the-art
models for various zero-shot NLP tasks yields performance gains with no
additional training.
