Sentence embeddings enable us to capture the semantic similarity of short texts. Most sentence embedding models are trained for general semantic textual similarity (STS) tasks. Therefore, to use sentence embeddings in a particular domain, the model must be adapted to it in order to achieve good results. Usually, this is done by fine-tuning the entire sentence embedding model for the domain of interest. While this approach yields state-of-the-art results, all of the model's weights are updated during fine-tuning, making this method resource-intensive. Therefore, instead of fine-tuning entire sentence embedding models for each target domain individually, we propose to train lightweight adapters. These domain-specific adapters do not require fine-tuning all underlying sentence embedding model parameters. Instead, we only train a small number of additional parameters while keeping the weights of the underlying sentence embedding model fixed. Training domain-specific adapters allows always using the same base model and only exchanging the domain-specific adapters to adapt sentence embeddings to a specific domain. We show that using adapters for parameter-efficient domain adaptation of sentence embeddings yields competitive performance within 1% of a domain-adapted, entirely fine-tuned sentence embedding model while only training approximately 3.6% of the parameters.

翻译：句子嵌入使我们能够捕捉短文本的语义相似性。大多数句子嵌入模型是为通用语义文本相似性（STS）任务训练的。因此，要在特定领域使用句子嵌入，必须对模型进行适配以获得良好结果。通常，这通过针对目标领域微调整个句子嵌入模型来实现。尽管这种方法能产生最先进的结果，但微调过程中会更新模型的所有权重，使该方法资源密集。因此，我们提议训练轻量级适配器，而非为每个目标领域单独微调整个句子嵌入模型。这些领域特异性适配器无需微调底层句子嵌入模型的所有参数。相反，我们仅训练少量额外参数，同时保持底层句子嵌入模型的权重固定。训练领域特异性适配器允许始终使用相同的基础模型，只需更换领域特异性适配器即可将句子嵌入适配到特定领域。我们证明，使用适配器进行句子嵌入的参数高效领域适配，其性能与完全微调的领域适配句子嵌入模型相比具有竞争力（差距在1%以内），而仅训练约3.6%的参数。