Prompting Language Models for Linguistic Structure
This work addresses the open question of linguistic generalization in PLMs for researchers in natural language processing, though it is incremental as it builds on existing prompting methods.
The researchers tackled the problem of determining whether pretrained language models (PLMs) rely on generalizable linguistic understanding or surface-level patterns by developing a structured prompting approach for zero- and few-shot sequence tagging tasks like part-of-speech tagging, named entity recognition, and sentence chunking, demonstrating strong few-shot performance and showing that PLMs can retrieve linguistic structure with arbitrary labels.
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 understanding versus surface-level lexical patterns. To test this, we present a structured prompting approach for linguistic structured 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, demonstrating strong few-shot performance in all cases. We also find that while PLMs contain significant prior knowledge of task labels due to task leakage into the pretraining corpus, structured prompting can also retrieve linguistic structure with arbitrary labels. These findings indicate that the in-context learning ability and linguistic knowledge of PLMs generalizes beyond memorization of their training data.