  Despite impressive results of language models for named entity recognition
(NER), their generalization to varied textual genres, a growing entity type
set, and new entities remains a challenge. Collecting thousands of annotations
in each new case for training or fine-tuning is expensive and time-consuming.
In contrast, humans can easily identify named entities given some simple
instructions. Inspired by this, we challenge the reliance on large datasets and
study pre-trained language models for NER in a meta-learning setup. First, we
test named entity typing (NET) in a zero-shot transfer scenario. Then, we
perform NER by giving few examples at inference. We propose a method to select
seen and rare / unseen names when having access only to the pre-trained model
and report results on these groups. The results show: auto-regressive language
models as meta-learners can perform NET and NER fairly well especially for
regular or seen names; name irregularity when often present for a certain
entity type can become an effective exploitable cue; names with words foreign
to the model have the most negative impact on results; the model seems to rely
more on name than context cues in few-shot NER.
