Benchmarking Complex Multimodal Document Processing Pipelines: A Unified Evaluation Framework for Enterprise AI
The landscape of enterprise document AI is increasingly dominated by complex pipelines that encompass various stages such as parsing, indexing, retrieval, and generation. Despite extensive research on individual components, a comprehensive evaluation of the entire system remains a significant challenge. A recent study introduces EnterpriseDocBench, a novel framework aimed at bridging this gap by enabling a unified evaluation of the complete document processing pipeline.
EnterpriseDocBench: A Comprehensive Evaluation Tool
EnterpriseDocBench is designed to assess multiple aspects of document processing, including:
- Parsing Fidelity: The accuracy with which documents are parsed.
- Indexing Efficiency: The speed and effectiveness of indexing processes.
- Retrieval Relevance: The quality of the documents retrieved in response to queries.
- Generation Groundedness: The reliability of generated responses based on the input documents.
To test the framework, researchers employed a diverse corpus consisting of public, permissively licensed documents from six distinct enterprise domains, five of which were utilized in the current pilot study. They ran three different retrieval pipelines through the corpus: BM25, dense embedding, and a hybrid approach, all utilizing the same GPT-5 generator for document generation.
Key Findings from the Study
The study yielded several intriguing insights into the performance of the evaluated pipelines:
- The hybrid retrieval method slightly outperformed BM25, achieving an nDCG@5 score of 0.92 compared to BM25’s 0.91. Both methods significantly outperformed the dense embedding approach, which scored 0.83.
- Interestingly, the study found that hallucination rates, or the generation of incorrect or fabricated information, did not increase consistently with document length. Short documents (28.1% hallucination rate) and very long documents (23.8% hallucination rate) exhibited higher hallucination rates compared to medium-length documents (9.2%).
- Cross-stage correlations among parsing, retrieval, and generation were notably weak. For instance, the correlation between parsing and retrieval was only r=0.14, and between retrieval and generation, it was a mere 0.02. These results challenge the assumption that quality improves in a cascading manner through the pipeline.
Accuracy and Completeness: A Surprising Discrepancy
One of the most unexpected findings was the contrast between factual accuracy and answer completeness. The system demonstrated an impressive factual accuracy rate of 85.5% on stated claims. However, the completeness of answers averaged only 0.40, indicating that while the system provides accurate responses, it often omits significant information. This gap is crucial for real-world applications, as completeness may be more important than sheer accuracy.
Future Directions and Open Source Initiative
The study also outlined three reference architectures: ColPali, ColQwen2, and agentic complexity-based routing. While these architectures have yet to be integrated into a complete end-to-end system, they represent significant steps toward enhancing document processing capabilities. The researchers plan to release the framework, metrics, baselines, and collection scripts as open-source resources upon acceptance of their findings.
In conclusion, EnterpriseDocBench provides a vital tool for evaluating complex multimodal document processing pipelines, offering insights that can guide future improvements in enterprise AI applications.
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