  Recent advances in prompt-based learning have shown strong results on
few-shot text classification by using cloze-style templates. Similar attempts
have been made on named entity recognition (NER) which manually design
templates to predict entity types for every text span in a sentence. However,
such methods may suffer from error propagation induced by entity span
detection, high cost due to enumeration of all possible text spans, and
omission of inter-dependencies among token labels in a sentence. Here we
present a simple demonstration-based learning method for NER, which lets the
input be prefaced by task demonstrations for in-context learning. We perform a
systematic study on demonstration strategy regarding what to include (entity
examples, with or without surrounding context), how to select the examples, and
what templates to use. Results on in-domain learning and domain adaptation show
that the model's performance in low-resource settings can be largely improved
with a suitable demonstration strategy (e.g., a 4-17% improvement on 25 train
instances). We also find that good demonstration can save many labeled examples
and consistency in demonstration contributes to better performance.
