Long-Tail Crisis in Nearest Neighbor Language Models
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arXiv
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| Hauptverfasser: | , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866908288485949440 |
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| author | Nishida, Yuto Morishita, Makoto Deguchi, Hiroyuki Kamigaito, Hidetaka Watanabe, Taro |
| author_facet | Nishida, Yuto Morishita, Makoto Deguchi, Hiroyuki Kamigaito, Hidetaka Watanabe, Taro |
| contents | The $k$-nearest-neighbor language model ($k$NN-LM), one of the retrieval-augmented language models, improves the perplexity for given text by directly accessing a large datastore built from any text data during inference. A widely held hypothesis for the success of $k$NN-LM is that its explicit memory, i.e., the datastore, enhances predictions for long-tail phenomena. However, prior works have primarily shown its ability to retrieve long-tail contexts, leaving the model's performance remain underexplored in estimating the probabilities of long-tail target tokens during inference. In this paper, we investigate the behavior of $k$NN-LM on low-frequency tokens, examining prediction probability, retrieval accuracy, token distribution in the datastore, and approximation error of the product quantization. Our experimental results reveal that $k$NN-LM does not improve prediction performance for low-frequency tokens but mainly benefits high-frequency tokens regardless of long-tail contexts in the datastore. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2503_22426 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Long-Tail Crisis in Nearest Neighbor Language Models Nishida, Yuto Morishita, Makoto Deguchi, Hiroyuki Kamigaito, Hidetaka Watanabe, Taro Computation and Language The $k$-nearest-neighbor language model ($k$NN-LM), one of the retrieval-augmented language models, improves the perplexity for given text by directly accessing a large datastore built from any text data during inference. A widely held hypothesis for the success of $k$NN-LM is that its explicit memory, i.e., the datastore, enhances predictions for long-tail phenomena. However, prior works have primarily shown its ability to retrieve long-tail contexts, leaving the model's performance remain underexplored in estimating the probabilities of long-tail target tokens during inference. In this paper, we investigate the behavior of $k$NN-LM on low-frequency tokens, examining prediction probability, retrieval accuracy, token distribution in the datastore, and approximation error of the product quantization. Our experimental results reveal that $k$NN-LM does not improve prediction performance for low-frequency tokens but mainly benefits high-frequency tokens regardless of long-tail contexts in the datastore. |
| title | Long-Tail Crisis in Nearest Neighbor Language Models |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2503.22426 |