A large-scale pipeline for LLM-assisted corpus annotation: variation and change in the English consider construction
arXiv:2510.12306v3 Announce Type: replace Abstract: As natural language corpora expand at an unprecedented rate, manual annotation remains a significant methodological bottleneck in corpus linguistic work. We address this challenge by presenting a scalable pipeline for automating grammatical annotation in voluminous corpora using large language models (LLMs). Unlike previous supervised and iterative approaches, our method employs a four-phase workflow: prompt engineering, pre-hoc evaluation, automated batch processing, and post-hoc validation. We demonstrate the pipeline's accessibility and ef
The rapid advancement and increased accessibility of large language models (LLMs) are enabling novel applications in traditionally manual, labor-intensive fields like corpus linguistics.
This development significantly reduces a major bottleneck in linguistic research, accelerating the creation of vast, annotated datasets crucial for further AI development and understanding of language.
The speed and scale of corpus annotation are no longer constrained by human annotator capacity, shifting the focus to LLM pipeline design and validation practices.
- · Corpus linguistics researchers
- · NLP developers
- · AI model trainers
- · Data annotation platform providers
- · Manual data annotators (for certain tasks)
- · Traditional, slow corpus development methodologies
Faster and larger linguistic datasets become available for various research and application areas.
Improved language understanding in AI models due to richer and more diverse training data.
The development of LLM-assisted annotation tools could extend to other complex data annotation challenges beyond linguistics.
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