Year: 2025
Type: conference-paper
Abstract: We present a method for labeling spans of text with large language models (LLMs) and apply it to the task of identifying shell language, language which plays a structural or connective role without constituting the main content of a text.We compare several recent LLMs by evaluating their "annotations" against a small human-curated test set, and train a smaller supervised model on thousands of LLM-annotated examples.The described method enables workflows that can learn complex or nuanced linguistic phenomena without tedious, large-scale hand-annotations of training data or specialized feature engineering.
Authors: Phoebe Mulcaire, Nitin Madnani
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: hybrid
OpenAlex ID: W4412889172