Summary of Mmsearch: Benchmarking the Potential Of Large Models As Multi-modal Search Engines, by Dongzhi Jiang et al.
MMSearch: Benchmarking the Potential of Large Models as Multi-modal Search Engines
by Dongzhi Jiang, Renrui Zhang, Ziyu Guo, Yanmin Wu, Jiayi Lei, Pengshuo Qiu, Pan Lu, Zehui Chen, Chaoyou Fu, Guanglu Song, Peng Gao, Yu Liu, Chunyuan Li, Hongsheng Li
First submitted to arxiv on: 19 Sep 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR)
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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 The paper explores the potential of Large Multimodal Models (LMMs) in multimodal search, a field that has been largely neglected by current AI search engines. The authors design a pipeline called MMSearch-Engine to empower LMMs with multimodal search capabilities and introduce MMSearch, a comprehensive evaluation benchmark to assess their performance. The curated dataset contains 300 manually collected instances spanning 14 subfields, which ensures the correct answer can only be obtained through searching. The authors conduct extensive experiments on closed-source and open-source LMMs, finding that GPT-4o with MMSearch-Engine achieves the best results, surpassing commercial products like Perplexity Pro. The paper also presents error analysis and an ablation study to guide future development of multimodal AI search engines. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper is about using big computer models (Large Multimodal Models) to help us find things on the internet that have words and pictures together. Right now, most search engines can only show you text results, but this could change with LMMs. The authors created a special way for LMMs to search called MMSearch-Engine and tested it on many different models. They found that one model, GPT-4o, worked really well when used with MMSearch-Engine. This is important because it could help us find what we’re looking for more easily online. |
Keywords
» Artificial intelligence » Gpt » Perplexity
