LLMs for Data Annotation
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A survey that maps the rapidly growing literature on using LLMs to generate, evaluate, and learn from data annotations.
Three pillars: LLM-based annotation generation, LLM-generated annotation assessment, and learning-with-LLM-annotations - giving a clean mental model for the space.
Method taxonomy: Organizes prompting, chain-of-thought, self-consistency, and agent-based approaches to annotation across text, code, and multimodal data.
Quality assessment: Reviews techniques for detecting hallucinated or low-quality LLM annotations, including verifier models and auxiliary labeling.
Practitioner guide: Concludes with open challenges and concrete recommendations for teams trying to replace costly human labels with LLM-generated ones without collapsing downstream quality.
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