Summary of Implicitave: An Open-source Dataset and Multimodal Llms Benchmark For Implicit Attribute Value Extraction, by Henry Peng Zou et al.
ImplicitAVE: An Open-Source Dataset and Multimodal LLMs Benchmark for Implicit Attribute Value Extraction
by Henry Peng Zou, Vinay Samuel, Yue Zhou, Weizhi Zhang, Liancheng Fang, Zihe Song, Philip S. Yu, Cornelia Caragea
First submitted to arxiv on: 24 Apr 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Information Retrieval (cs.IR); Machine Learning (cs.LG)
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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 ImplicitAVE, a publicly available multimodal dataset for implicit attribute value extraction. The existing datasets focus on explicit attributes, neglecting implicit ones, and lack product images, human inspection, and domain diversity. To address these limitations, the authors curate and expand the MAVE dataset to include 68k training and 1.6k testing data across five domains, making it a comprehensive benchmark for multimodal large language models (MLLMs). They evaluate six recent MLLMs with eleven variants, revealing that implicit value extraction remains a challenging task. The paper’s contributions are the development and release of ImplicitAVE and the exploration and benchmarking of various MLLMs for implicit AVE. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper creates a new dataset called ImplicitAVE to help machines better understand products. Right now, most datasets only include information that is clearly written about products, like prices or sizes. But there are many other important details that aren’t mentioned, like how the product looks or what it’s used for. The new dataset includes all this extra information and makes it available for anyone to use. The authors also tested some machine learning models on the new dataset and found out that they still have a lot to learn about understanding products. |
Keywords
» Artificial intelligence » Machine learning
