AI Research
From Pixels to Pairs: A Comprehensive Benchmark of LLM-Based Key-Value Extraction in Noisy Document Settings
Quantifying the resilience of large language models against OCR-induced errors in document data extraction.
Large language models (LLMs) are increasingly used for structured information extraction from documents, yet their behavior under realistic OCR noise remains poorly understood. We present a systematic benchmark of open-source instruction-tuned LLMs for key-value pair (KVP) extrac...
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ArXiv CS.CL
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ArXiv CS.CL
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This research addresses a critical gap in enterprise AI by quantifying how OCR noise impacts the reliability of automated document processing systems. By benchmarking LLMs against real-world degradation, the findings provide a essential roadmap for building more resilient data extraction pipelines.
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