Seeking Rational Demonstrations for Large Language Models: A Domain Generalization Approach to Unsupervised Cross-Domain Keyphrase Generation

Unsupervised cross-domain keyphrase generation (KPG) is crucial in real-world natural language processing scenarios.However, the accuracy of up-to-date approaches is limited by the distribution shift between source and target domain, which stems from the cross-domain field.Large language models (LLMs) offer potential for the cross-domain keyphrase generation tasks due to their strong generalization abilities, facilitated by providing demonstrations relevant to the target task.Nevertheless, it is often difficult to obtain labeled samples from the target domain.To address this challenge, this paper aims to seek rational demonstrations from the source domain, thereby improving the LLMs' ability in the unsupervised crossdomain keyphrase generation setting.Specifically, we design a novel domain-aware retrieval model on the source domain.Guided by insights from domain generalization theory, we introduce two generalization terms, one for cross-domain relevance and another for each domain consistency to better support retrieval of rational demonstrations.By the retrieved source-domain demonstrations and distancebased relevant score, the proposed approach achieves optimal accuracy.Comprehensive experiments on widely used cross-domain KPG benchmarks demonstrate our approach's stateof-the-art performance and effectiveness.