Summary of Captions Speak Louder Than Images (caslie): Generalizing Foundation Models For E-commerce From High-quality Multimodal Instruction Data, by Xinyi Ling et al.
Captions Speak Louder than Images (CASLIE): Generalizing Foundation Models for E-commerce from High-quality Multimodal Instruction Data
by Xinyi Ling, Bo Peng, Hanwen Du, Zhihui Zhu, Xia Ning
First submitted to arxiv on: 22 Oct 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); 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 introduces Multimodal Foundation Models (MFMs) for e-commerce applications, highlighting the challenges of leveraging multimodal data due to a lack of high-quality benchmark datasets and effective information integration methods. The authors propose MMECInstruct, a large-scale multimodal instruction dataset for e-commerce, and CASLIE, a framework for integrating multimodal information. They fine-tune MFMs within CASLIE and demonstrate substantial performance improvements over baseline models in both in-domain and out-of-domain settings. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper makes e-commerce better by using lots of different types of data together. It’s hard to find big datasets that have all the right kinds of information, so they made one called MMECInstruct. They also created a way to combine all this data, called CASLIE. When they used these new tools, their models did much better than others at predicting what people want to buy online. |
