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arxiv:2406.07230

Needle In A Multimodal Haystack

Published on Jun 11
· Submitted by Weiyun1025 on Jun 17
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Abstract

With the rapid advancement of multimodal large language models (MLLMs), their evaluation has become increasingly comprehensive. However, understanding long multimodal content, as a foundational ability for real-world applications, remains underexplored. In this work, we present Needle In A Multimodal Haystack (MM-NIAH), the first benchmark specifically designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents. Our benchmark includes three types of evaluation tasks: multimodal retrieval, counting, and reasoning. In each task, the model is required to answer the questions according to different key information scattered throughout the given multimodal document. Evaluating the leading MLLMs on MM-NIAH, we observe that existing models still have significant room for improvement on these tasks, especially on vision-centric evaluation. We hope this work can provide a platform for further research on long multimodal document comprehension and contribute to the advancement of MLLMs. Code and benchmark are released at https://github.com/OpenGVLab/MM-NIAH.

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Needle In A Multimodal Haystack (MM-NIAH) is a comprehensive benchmark designed to systematically evaluate the capability of existing MLLMs to comprehend long multimodal documents. This benchmark requires the model to answer specific questions according to the key information scattered throughout the multimodal document. The evaluation data in MM-NIAH consists of three tasks: retrieval, counting, and reasoning. The needles are inserted into either text or images in the documents. Those inserted into text are termed text needles, whereas those within images are referred to as image needles.
Experimental results show that performance of Gemini-1.5 on tasks with image needles is no better than a random guess.

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