Large language models (LLMs) have showcased their capability with few-shot
inference known as in-context learning. However, in-domain demonstrations are
not always readily available in real scenarios, leading to cross-domain
in-context learning. Besides, LLMs are still facing challenges in long-tail
knowledge in unseen and unfamiliar domains. The above limitations demonstrate
the necessity of Unsupervised Domain Adaptation (UDA). In this paper, we study
the UDA problem under an in-context learning setting to adapt language models
from the source domain to the target domain without any target labels. The core
idea is to retrieve a subset of cross-domain elements that are the most similar
to the query, and elicit language model to adapt in an in-context manner by
learning both target domain distribution and the discriminative task signal
simultaneously with the augmented cross-domain in-context examples. We devise
different prompting and training strategies, accounting for different LM
architectures to learn the target distribution via language modeling. With
extensive experiments on Sentiment Analysis (SA) and Named Entity Recognition
(NER) tasks, we thoroughly study the effectiveness of ICL for domain transfer
and demonstrate significant improvements over baseline models.

通过无监督的领域自适应方法以及在上下文学习环境下的查询元素子集检索策略，我们研究了适应语言模型从源领域到目标领域的问题，以学习目标领域分布并通过语言建模实现任务信号的适应，从而在情感分析和命名实体识别任务中实现了显著的性能提升。