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What is Scalable Historical Data Annotation Using LLMs about?
This paper presents a new method for extracting knowledge from large language models (LLMs) to create structured historical datasets, comparing the generated data with human-annotated datasets from 10,000 BC to 2000 CE. The study finds that while generative AI can produce valuable historical annotations across various factors, its performance is inconsistent and influenced by regional complexities and data quality. The authors emphasize the need for precise instructions and prompt engineering to enhance the
- Author
- stevemills
- Language
- EN