Summary of Eiven: Efficient Implicit Attribute Value Extraction Using Multimodal Llm, by Henry Peng Zou et al.
EIVEN: Efficient Implicit Attribute Value Extraction using Multimodal LLM
by Henry Peng Zou, Gavin Heqing Yu, Ziwei Fan, Dan Bu, Han Liu, Peng Dai, Dongmei Jia, Cornelia Caragea
First submitted to arxiv on: 13 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)
GrooveSquid.com Paper Summaries
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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 EIVEN, a generative framework that leverages large language models (LLMs) and vision encoders to extract product attribute values from multimodal e-commerce data. The proposed approach reduces reliance on labeled data by utilizing the rich knowledge embedded in pre-trained LLMs and vision encoders. A novel Learning-by-Comparison technique is also introduced to mitigate model confusion by comparing and identifying differences between attribute values. Experimental results show that EIVEN outperforms existing methods while requiring less labeled data. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper develops a new way to extract important information from pictures and text in e-commerce. This helps make shopping easier and more efficient for customers, as well as reduces the work needed by retailers. The approach uses special computer models that have been trained on lots of data, which helps them learn how to recognize patterns and make good decisions. The paper also introduces a new technique called Learning-by-Comparison, which helps prevent mistakes by comparing different values. This can be useful when there are many similar things being compared. |
