Rethinking Document Information Extraction Datasets for LLMs
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.
