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- W4321593827 abstract "Fine-tuning a language model on a new domain is standard practice for domain adaptation. However, it can be infeasible when it comes to modern large-scale language models such as GPT-3, which can only be accessed through APIs, making it difficult to access the internal parameters of the model. In this paper, we propose $k$NN-Adapter, a method to effectively adapt these black-box large language models (LLMs) to a new domain. The $k$NN-Adapter builds on top of the retrieval-augmented language model, and adaptively learns to interpolate the output of the language model with retrieval results from a datastore consisting of the target domain data. Our experiments on four different domains demonstrate that $k$NN-Adapter significantly improves perplexity, and works particularly well in settings with limited access to LLMs. Additionally, we show that $k$NN-Adapter is more effective than fine-tuning when the amount of training data is limited. We also release a dataset to encourage further study." @default.
- W4321593827 created "2023-02-24" @default.
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- W4321593827 date "2023-02-21" @default.
- W4321593827 modified "2023-09-26" @default.
- W4321593827 title "$k$NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models" @default.
- W4321593827 doi "https://doi.org/10.48550/arxiv.2302.10879" @default.
- W4321593827 hasPublicationYear "2023" @default.
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