Nearest Neighbor Zero-Shot Inference

Weijia Shi, Julian Michael, Suchin Gururangan, Luke Zettlemoyer

Abstract
Retrieval-augmented language models (LMs) use non-parametric memory to substantially outperform their non-retrieval counterparts on perplexity-based evaluations, but it is an open question whether they achieve similar gains in few- and zero-shot end-task accuracy. We extensively study one such model, the k-nearest neighbor LM (kNN-LM), showing that the gains marginally transfer. The main challenge is to achieve coverage of the verbalizer tokens that define the different end-task class labels. To address this challenge, we also introduce kNN-Prompt, a simple and effective kNN-LM with automatically expanded fuzzy verbalizers (e.g. to expand “terrible” to also include “silly” and other task-specific synonyms for sentiment classification). Across nine diverse end-tasks, using kNN-Prompt with GPT-2 large yields significant performance boosts over strong zeroshot baselines (13.4% absolute improvement over the base LM on average). We also show that other advantages of non-parametric augmentation hold for end tasks; kNN-Prompt is effective for domain adaptation with no further training, and gains increase with the size of the retrieval model.
Anthology ID:
2022.emnlp-main.214
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
3254–3265
Language:
URL:
https://aclanthology.org/2022.emnlp-main.214
DOI:
10.18653/v1/2022.emnlp-main.214
Bibkey:
Cite (ACL):
Weijia Shi, Julian Michael, Suchin Gururangan, and Luke Zettlemoyer. 2022. Nearest Neighbor Zero-Shot Inference. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 3254–3265, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
Nearest Neighbor Zero-Shot Inference (Shi et al., EMNLP 2022)
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PDF:
https://preview.aclanthology.org/nschneid-patch-2/2022.emnlp-main.214.pdf
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