Truncation Sampling as Language Model Desmoothing

John Hewitt, Christopher Manning, Percy Liang


Abstract
Long samples of text from neural language models can be of poor quality. Truncation sampling algorithms–like top-p or top-k—address this by setting some words’ probabilities to zero at each step. This work investigates why these methods are important, and how to improve them. We propose thinking of a neural language model as a mixture of a true distribution and a smoothing distribution that avoids infinite perplexity. In this light, truncation algorithms aim to perform desmoothing, estimating a subset of the support of the true distribution. Finding a good subset is crucial: we show that top-p unnecessarily truncates high-probability words, for example causing it to truncate all words but Trump for a document that starts with Donald. We introduce eta-sampling, which truncates words below an entropy-dependent probability threshold. Compared to previous algorithms, our eta-sampling generates more plausible long documents according to humans, is better at breaking out of repetition, and behaves more reasonably on a battery of test distributions.
Anthology ID:
2022.findings-emnlp.249
Volume:
Findings of the Association for Computational Linguistics: EMNLP 2022
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
Findings
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3414–3427
Language:
URL:
https://aclanthology.org/2022.findings-emnlp.249
DOI:
10.18653/v1/2022.findings-emnlp.249
Bibkey:
Cite (ACL):
John Hewitt, Christopher Manning, and Percy Liang. 2022. Truncation Sampling as Language Model Desmoothing. In Findings of the Association for Computational Linguistics: EMNLP 2022, pages 3414–3427, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Truncation Sampling as Language Model Desmoothing (Hewitt et al., Findings 2022)
Copy Citation:
PDF:
https://aclanthology.org/2022.findings-emnlp.249.pdf