Summary of Needle in a Multimodal Haystack, by Weiyun Wang et al.
Needle In A Multimodal Haystack
by Weiyun Wang, Shuibo Zhang, Yiming Ren, Yuchen Duan, Tiantong Li, Shuo Liu, Mengkang Hu, Zhe Chen, Kaipeng Zhang, Lewei Lu, Xizhou Zhu, Ping Luo, Yu Qiao, Jifeng Dai, Wenqi Shao, Wenhai Wang
First submitted to arxiv on: 11 Jun 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary A novel benchmark, Needle In A Multimodal Haystack (MM-NIAH), is introduced to assess the comprehension capabilities of multimodal large language models (MLLMs) in understanding long multimodal documents. This benchmark consists of three evaluation tasks: multimodal retrieval, counting, and reasoning, which require MLLMs to answer questions based on scattered information within the document. The leading MLLMs are evaluated on MM-NIAH, revealing significant room for improvement, particularly on vision-centric evaluations. This work provides a platform for further research on long multimodal document comprehension and contributes to the advancement of MLLMs. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary A new way to test how well computers understand mixed media content is developed. The goal is to measure how well large language models can read and understand long documents that combine different types of information, like text, images, and audio. Three tasks are used to test the models: finding specific information in a document, counting objects or actions mentioned, and answering questions based on what’s written or shown. Currently available models don’t do very well on these tasks, especially when it comes to understanding visual content. This new benchmark can help researchers improve how well computers understand long documents and make better language models. |
