  Few-shot Named Entity Recognition (NER) is imperative for entity tagging in
limited resource domains and thus received proper attention in recent years.
Existing approaches for few-shot NER are evaluated mainly under in-domain
settings. In contrast, little is known about how these inherently faithful
models perform in cross-domain NER using a few labeled in-domain examples. This
paper proposes a two-step rationale-centric data augmentation method to improve
the model's generalization ability. Results on several datasets show that our
model-agnostic method significantly improves the performance of cross-domain
NER tasks compared to previous state-of-the-art methods, including the data
augmentation and prompt-tuning methods. Our codes are available at
https://github.com/lifan-yuan/FactMix.
