Papers › You can't pick your neighbors, or can you? When and how to rely on retrieval in the $k$NN-LM

You can't pick your neighbors, or can you? When and how to rely on retrieval in the $k$NN-LM

28 Oct 2022arXiv:2210.15859archive 2025-07-28

Andrew Drozdov, Shufan Wang, Razieh Rahimi, Andrew McCallum, Hamed Zamani, Mohit Iyyer

Retrieval-enhanced language models (LMs), which condition their predictions on text retrieved from large external datastores, have recently shown significant perplexity improvements compared to standard LMs. One such approach, the $k$NN-LM, interpolates any existing LM's predictions with the output of a k-nearest neighbors model and requires no additional training. In this paper, we explore the importance of lexical and semantic matching in the context of items retrieved by $k$NN-LM. We find two trends: (1) the presence of large overlapping n-grams between the datastore and evaluation set plays an important factor in strong performance, even when the datastore is derived from the training data; and (2) the $k$NN-LM is most beneficial when retrieved items have high semantic similarity with the query. Based on our analysis, we define a new formulation of the $k$NN-LM that uses retrieval quality to assign the interpolation coefficient. We empirically measure the effectiveness of our approach on two English language modeling datasets, Wikitext-103 and PG-19. Our re-formulation of the $k$NN-LM is beneficial in both cases, and leads to nearly 4% improvement in perplexity on the Wikitext-103 test set.

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Tasks

Language ModelingLanguage ModellingRetrievalSemantic SimilaritySemantic Textual Similarity

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Language Modelling WikiText-103 kNN-LM w/ Adaptive Coefficient Number of params 247M #9 of 89 Archive leaderboard report
Language Modelling WikiText-103 kNN-LM w/ Adaptive Coefficient Test perplexity 15.5 #9 of 89 Archive leaderboard report
Language Modelling WikiText-103 kNN-LM w/ Adaptive Coefficient Validation perplexity 15.72 #9 of 89 Archive leaderboard report

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