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The paper "Rethinking Document Information Extraction" presents a critical analysis of current document information extraction (IE) datasets and their limitations in evaluating LLM capabilities. The authors identify significant gaps between traditional IE evaluation methods and the actual capabilities of modern LLMs. The research introduces a novel multi-format evaluation framework that considers both structured and unstructured document understanding. The study reveals that existing benchmarks often underestimate LLM performance by focusing too narrowly on specific formats or extraction tasks. Key findings demonstrate that LLMs can effectively handle complex document structures, cross-referencing, and contextual understanding when evaluated appropriately. The paper proposes new dataset creation guidelines and evaluation metrics that better align with real-world document processing challenges. This work provides valuable insights for improving document IE systems and suggests a paradigm shift in how we assess LLM performance on document understanding tasks.
