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M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding And A Retrieval-Aware Tuning Framework

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arxiv 2411.06176 v1 pith:MHXCB3HK submitted 2024-11-09 cs.CL

Yew Ken Chia , Liying Cheng , Hou Pong Chan , Chaoqun Liu , Maojia Song , Sharifah Mahani Aljunied , Soujanya Poria , Lidong Bing This is my paper · ORCID
classification cs.CL
keywords documentsmultimodalmodelstuningbenchmarkframeworkapproachautomated
verification ladder T0 review T1 audit T2 compute T3 formal
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The ability to understand and answer questions over documents can be useful in many business and practical applications. However, documents often contain lengthy and diverse multimodal contents such as texts, figures, and tables, which are very time-consuming for humans to read thoroughly. Hence, there is an urgent need to develop effective and automated methods to aid humans in this task. In this work, we introduce M-LongDoc, a benchmark of 851 samples, and an automated framework to evaluate the performance of large multimodal models. We further propose a retrieval-aware tuning approach for efficient and effective multimodal document reading. Compared to existing works, our benchmark consists of more recent and lengthy documents with hundreds of pages, while also requiring open-ended solutions and not just extractive answers. To our knowledge, our training framework is the first to directly address the retrieval setting for multimodal long documents. To enable tuning open-source models, we construct a training corpus in a fully automatic manner for the question-answering task over such documents. Experiments show that our tuning approach achieves a relative improvement of 4.6% for the correctness of model responses, compared to the baseline open-source models. Our data, code, and models are available at https://multimodal-documents.github.io.

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Cited by 7 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

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  2. VisR-Bench: An Empirical Study on Visual Retrieval-Augmented Generation for Multilingual Long Document Understanding

    cs.CV 2025-08 conditional novelty 6.0 of 10

    A multilingual, multi-page document retrieval benchmark with 35K+ QA pairs shows MLLM retrievers lead but still fail on tables and low-resource languages.

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    Multimodal context-learning benchmark CLBench-V separates grounding, information application, and knowledge acquisition; the best evaluated model scores 0.2847.

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