M-Longdoc: A Benchmark For Multimodal Super-Long Document Understanding
  And A Retrieval-Aware Tuning Framework
Authors: name: Yew Ken Chia, name: Liying Cheng, name: Hou Pong Chan, name: Chaoqun Liu, name: Maojia Song, name: Sharifah Mahani Aljunied, name: Soujanya Poria, name: Lidong Bing

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.