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This paper investigates whether large language models encode syntactic abstractions like phase boundaries that are not captured by Universal Dependencies, using structural probes on wh-movement stimuli with invariant UD distances, finding evidence across 13 LLMs for phase-structure representations that are causally active.
This paper presents a reproducible pipeline for building Universal Dependencies-style parsing resources for Katharevousa Greek parliamentary text, including OCR reconstruction, LLM-assisted annotation, and evaluation of multiple parsers. The best model (XLM-R) achieves 0.8893 UPOS accuracy and 0.5162 LAS, significantly outperforming off-the-shelf baselines.
CAIT is an open-source toolkit for syntactic parsing of child-adult interactions, featuring a dependency parser, POS tagger, and construction tagger trained on the UD-English-CHILDES treebank, outperforming general English parsers like SpaCy and Stanza.
Georgetown researchers boost low-resource Coptic-to-English translation by augmenting in-context prompts with Universal Dependencies syntactic parses alongside bilingual glosses, setting a new state-of-the-art.