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

Probing Pre-trained Auto-regressive Language Models for Named Entity Typing and Recognition