Prompting Language Models for Linguistic Structure
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Although pretrained language models (PLMs) can be prompted to perform a wide range of language tasks, it remains an open question how much this ability comes from generalizable linguistic representations versus more surface-level lexical patterns. To test this, we present a structured prompting approach that can be used to prompt for linguistic structure prediction tasks, allowing us to perform zero- and few-shot sequence tagging with autoregressive PLMs. We evaluate this approach on part-of-speech tagging, named entity recognition, and sentence chunking and demonstrate strong few-shot performance in all cases. We also find that, though the surface forms of the tags provide some signal, structured prompting can retrieve linguistic structure even with arbitrary labels, indicating that PLMs contain this knowledge in a general manner robust to label choice.
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