MMR: Evaluating Reading Ability of Large Multimodal Models

• URL: http://arxiv.org/abs/2408.14594v1
• Date: Mon, 26 Aug 2024 19:26:50 GMT
• Title: MMR: Evaluating Reading Ability of Large Multimodal Models
• Authors: Jian Chen, Ruiyi Zhang, Yufan Zhou, Ryan Rossi, Jiuxiang Gu, Changyou Chen,
• Abstract summary: Large multimodal models (LMMs) have demonstrated impressive capabilities in understanding various types of image, including text-rich images. Current benchmarks fail to accurately reflect performance of different models. We propose the Multi-Modal Reading (MMR) benchmark in 11 diverse tasks to evaluate LMMs for text-rich image understanding.
• Score: 52.953316772123586
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Large multimodal models (LMMs) have demonstrated impressive capabilities in understanding various types of image, including text-rich images. Most existing text-rich image benchmarks are simple extraction-based question answering, and many LMMs now easily achieve high scores. This means that current benchmarks fail to accurately reflect performance of different models, and a natural idea is to build a new benchmark to evaluate their complex reasoning and spatial understanding abilities. In this work, we propose the Multi-Modal Reading (MMR) benchmark in 11 diverse tasks to evaluate LMMs for text-rich image understanding. MMR is the first text-rich image benchmark built on human annotations with the help of language models. By evaluating several state-of-the-art LMMs, including GPT-4o, it reveals the limited capabilities of existing LMMs underscoring the value of our benchmark.

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