  Pretrained language models (PLMs) have shown remarkable few-shot learning
capabilities when provided with properly formatted examples. However, selecting
the "best" examples remains an open challenge. We propose a complexity-based
prompt selection approach for sequence tagging tasks. This approach avoids the
training of a dedicated model for selection of examples, and instead uses
certain metrics to align the syntactico-semantic complexity of test sentences
and examples. We use both sentence- and word-level metrics to match the
complexity of examples to the (test) sentence being considered. Our results
demonstrate that our approach extracts greater performance from PLMs: it
achieves state-of-the-art performance on few-shot NER, achieving a 5% absolute
improvement in F1 score on the CoNLL2003 dataset for GPT-4. We also see large
gains of upto 28.85 points (F1/Acc.) in smaller models like GPT-j-6B.
