Document Haystack: A Long Context Multimodal Image/Document Understanding Vision LLM Benchmark
This addresses the problem of under-explored long document processing for researchers and developers in multimodal AI, though it is incremental as it focuses on benchmarking rather than model innovation.
The authors tackled the lack of benchmarks for evaluating Vision Language Models on long documents by introducing Document Haystack, a comprehensive dataset with 400 document variants and 8,250 questions, which revealed performance gaps in current models.
The proliferation of multimodal Large Language Models has significantly advanced the ability to analyze and understand complex data inputs from different modalities. However, the processing of long documents remains under-explored, largely due to a lack of suitable benchmarks. To address this, we introduce Document Haystack, a comprehensive benchmark designed to evaluate the performance of Vision Language Models (VLMs) on long, visually complex documents. Document Haystack features documents ranging from 5 to 200 pages and strategically inserts pure text or multimodal text+image "needles" at various depths within the documents to challenge VLMs' retrieval capabilities. Comprising 400 document variants and a total of 8,250 questions, it is supported by an objective, automated evaluation framework. We detail the construction and characteristics of the Document Haystack dataset, present results from prominent VLMs and discuss potential research avenues in this area.