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.
@article{arxiv.2110.08294,
title = {Coherence boosting: When your pretrained language model is not paying enough attention},
author = {Nikolay Malkin and Zhen Wang and Nebojsa Jojic},
journal= {arXiv preprint arXiv:2110.08294},
year = {2022}
}