Benchmarking Complex Multimodal Document Processing Pipelines: A Unified Evaluation Framework for Enterprise AI
📰 ArXiv cs.AI
Learn to evaluate complex multimodal document processing pipelines using a unified framework, crucial for enterprise AI applications
Action Steps
- Build a multimodal document processing pipeline using EnterpriseDocBench
- Configure the pipeline to evaluate parsing fidelity, indexing efficiency, retrieval relevance, and generation groundedness
- Test the pipeline on a corpus of public, permissively licensed documents
- Compare the results with existing benchmarks to identify areas for improvement
- Apply the unified evaluation framework to optimize the pipeline for enterprise AI applications
Who Needs to Know This
Data scientists, AI engineers, and software engineers working on document processing pipelines can benefit from this framework to evaluate and improve their systems
Key Insight
💡 A unified evaluation framework is necessary to assess the performance of multimodal document processing pipelines as a whole, rather than individual stages
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Key Takeaways
Learn to evaluate complex multimodal document processing pipelines using a unified framework, crucial for enterprise AI applications
Full Article
Title: Benchmarking Complex Multimodal Document Processing Pipelines: A Unified Evaluation Framework for Enterprise AI
Abstract:
arXiv:2604.26382v1 Announce Type: cross Abstract: Most enterprise document AI today is a pipeline. Parse, index, retrieve, generate. Each of those stages has been studied to death on its own -- what's still hard is evaluating the system as a whole. We built EnterpriseDocBench to take a swing at it: parsing fidelity, indexing efficiency, retrieval relevance, and generation groundedness, all on the same corpus. The corpus is built from public, permissively licensed documents across six enterprise
Abstract:
arXiv:2604.26382v1 Announce Type: cross Abstract: Most enterprise document AI today is a pipeline. Parse, index, retrieve, generate. Each of those stages has been studied to death on its own -- what's still hard is evaluating the system as a whole. We built EnterpriseDocBench to take a swing at it: parsing fidelity, indexing efficiency, retrieval relevance, and generation groundedness, all on the same corpus. The corpus is built from public, permissively licensed documents across six enterprise
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