LLM-Assisted Annotation
Overview
This field studies how large language models can help create labels and structured metadata for text, images, audio, video, and specialized records. Common approaches include prompt and codebook design, retrieval-augmented examples, model ensembles, confidence-based review, and training smaller classifiers on generated labels. These methods can reduce annotation time and cost, especially in low-resource or domain-specific settings, but their usefulness depends on careful validation against expert judgments and downstream tasks. A central challenge is that agreement and repeatability do not guarantee valid labels: results can shift with prompts, models, task definitions, and demographic cues, so human oversight and transparent error analysis remain important.
Latest papers
202 papers21 papers in the last four weeks, down 16% on the four weeks before. 0.1% of all new papers.