@inproceedings{min-etal-2023-nonparametric,
title = "Nonparametric Masked Language Modeling",
author = "Min, Sewon and
Shi, Weijia and
Lewis, Mike and
Chen, Xilun and
Yih, Wen-tau and
Hajishirzi, Hannaneh and
Zettlemoyer, Luke",
editor = "Rogers, Anna and
Boyd-Graber, Jordan and
Okazaki, Naoaki",
booktitle = "Findings of the Association for Computational Linguistics: ACL 2023",
month = jul,
year = "2023",
address = "Toronto, Canada",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-acl.132",
doi = "10.18653/v1/2023.findings-acl.132",
pages = "2097--2118",
abstract = "Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first nonparametric masked language model that replaces this softmax with a nonparametric distribution over every phrase in a reference corpus. NPM fills in the [MASK] solely from retrieving a token from a text corpus. We show that NPM can be efficiently trained with a contrastive objective and an in-batch approximation to full corpus retrieval. Zero-shot evaluation on 16 tasks including classification, fact probing and question answering demonstrates that NPM outperforms significantly larger parametric models, either with or without a retrieve-and-generate approach. It is particularly better at dealing with rare patterns (word senses or facts) and predicting rare or nearly unseen words (e.g., non-Latin script). We release the model and code at github.com/facebookresearch/NPM.",
}
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%0 Conference Proceedings
%T Nonparametric Masked Language Modeling
%A Min, Sewon
%A Shi, Weijia
%A Lewis, Mike
%A Chen, Xilun
%A Yih, Wen-tau
%A Hajishirzi, Hannaneh
%A Zettlemoyer, Luke
%Y Rogers, Anna
%Y Boyd-Graber, Jordan
%Y Okazaki, Naoaki
%S Findings of the Association for Computational Linguistics: ACL 2023
%D 2023
%8 July
%I Association for Computational Linguistics
%C Toronto, Canada
%F min-etal-2023-nonparametric
%X Existing language models (LMs) predict tokens with a softmax over a finite vocabulary, which can make it difficult to predict rare tokens or phrases. We introduce NPM, the first nonparametric masked language model that replaces this softmax with a nonparametric distribution over every phrase in a reference corpus. NPM fills in the [MASK] solely from retrieving a token from a text corpus. We show that NPM can be efficiently trained with a contrastive objective and an in-batch approximation to full corpus retrieval. Zero-shot evaluation on 16 tasks including classification, fact probing and question answering demonstrates that NPM outperforms significantly larger parametric models, either with or without a retrieve-and-generate approach. It is particularly better at dealing with rare patterns (word senses or facts) and predicting rare or nearly unseen words (e.g., non-Latin script). We release the model and code at github.com/facebookresearch/NPM.
%R 10.18653/v1/2023.findings-acl.132
%U https://aclanthology.org/2023.findings-acl.132
%U https://doi.org/10.18653/v1/2023.findings-acl.132
%P 2097-2118
Markdown (Informal)
[Nonparametric Masked Language Modeling](https://aclanthology.org/2023.findings-acl.132) (Min et al., Findings 2023)
ACL
- Sewon Min, Weijia Shi, Mike Lewis, Xilun Chen, Wen-tau Yih, Hannaneh Hajishirzi, and Luke Zettlemoyer. 2023. Nonparametric Masked Language Modeling. In Findings of the Association for Computational Linguistics: ACL 2023, pages 2097–2118, Toronto, Canada. Association for Computational Linguistics.