| Challenge: | Prior studies have shown that kNN-LM can retrieve long-tail contexts, leaving the model’s performance underexplored in estimating the probabilities of long-tailed target tokens. |
| Approach: | They investigate the behavior of kNN-LM on low-frequency tokens, examining prediction probability, retrieval accuracy, and token distribution in the datastore. |
| Outcome: | The proposed model improves the perplexity of given text by directly accessing a large datastore built from any text data during inference. |
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You can’t pick your neighbors, or can you? When and How to Rely on Retrieval in the kNN-LM (2022.findings-emnlp)
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Predicting Numerals in Text Using Nearest Neighbor Language Models (2023.findings-acl)
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