$k$NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models

Yangsibo Huang; Daogao Liu; Zexuan Zhong; Weijia Shi; Yin Tat Lee

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arXiv · 2302.10879

$k$NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models

Yangsibo Huang·Daogao Liu·Zexuan Zhong·Weijia Shi·Yin Tat Lee

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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.

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BibTeX RIS

Yangsibo Huang, Daogao Liu, Zexuan Zhong, Weijia Shi, Yin Tat Lee. 2023-02-21. $k$NN-Adapter: Efficient Domain Adaptation for Black-Box Language Models. https://arxiv.org/abs/2302.10879

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