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The author introduces 'llmog,' a tool that uses LLMs for auto-annotation of datasets, running locally with llama.cpp or vllm, or via external API, for users who prefer a UI over coding.
Introduces DocAnnot, a framework that uses a large vision-language model, OCR, and a spatially informed contextual matching algorithm to automatically generate training datasets for key information extraction from documents, reducing manual annotation effort. Evaluated on CORD and SROIE benchmarks, it achieves reasonable F1-scores and enables efficient human verification.