@inproceedings{bhardwaj-etal-2023-adaptation,
    title = "Adaptation Approaches for Nearest Neighbor Language Models",
    author = "Bhardwaj, Rishabh  and
      Polovets, George  and
      Sunkara, Monica",
    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.73",
    doi = "10.18653/v1/2023.findings-acl.73",
    pages = "1135--1146",
    abstract = "Semi-parametric Nearest Neighbor Language Models (kNN-LMs) have produced impressive gains over purely parametric LMs, by leveraging large-scale neighborhood retrieval over external memory datastores. However, there has been little investigation into adapting such models for new domains. This work attempts to fill that gap and suggests the following approaches for adapting kNN-LMs {---} 1) adapting the underlying LM (using Adapters), 2) expanding neighborhood retrieval over an additional adaptation datastore, and 3) adapting the weights (scores) of retrieved neighbors using a learned Rescorer module. We study each adaptation strategy separately, as well as the combined performance improvement through ablation experiments and an extensive set of evaluations run over seven adaptation domains. Our combined adaptation approach consistently outperforms purely parametric adaptation and zero-shot (kNN-LM) baselines that construct datastores from the adaptation data. On average, we see perplexity improvements of 17.1{\%} and 16{\%} for these respective baselines, across domains.",
}

Semi-parametric Nearest Neighbor Language Models (kNN-LMs) have produced impressive gains over purely parametric LMs, by leveraging large-scale neighborhood retrieval over external memory datastores. However, there has been little investigation into adapting such models for new domains. This work attempts to fill that gap and suggests the following approaches for adapting kNN-LMs {---} 1) adapting the underlying LM (using Adapters), 2) expanding neighborhood retrieval over an additional adaptation datastore, and 3) adapting the weights (scores) of retrieved neighbors using a learned Rescorer module. We study each adaptation strategy separately, as well as the combined performance improvement through ablation experiments and an extensive set of evaluations run over seven adaptation domains. Our combined adaptation approach consistently outperforms purely parametric adaptation and zero-shot (kNN-LM) baselines that construct datastores from the adaptation data. On average, we see perplexity improvements of 17.1{\%} and 16{\%} for these respective baselines, across domains.
Bhardwaj, Rishabh Polovets, George Sunkara, Monica
Adaptation Approaches for Nearest Neighbor Language Models