LLM Annotation

LLM annotation leverages large language models (LLMs) to automate the labeling of data for various natural language processing (NLP) tasks, aiming to reduce the cost and time associated with manual annotation. Current research focuses on optimizing LLM prompting strategies, developing methods to assess and improve LLM annotation accuracy (e.g., confidence-driven inference, multi-fidelity learning), and exploring the integration of LLM annotations with human expertise in a collaborative workflow. This approach holds significant promise for improving the efficiency and scalability of NLP research and development, particularly in resource-constrained settings and for tasks involving large or complex datasets.