Summary of Fine-grainedly Synthesize Streaming Data Based on Large Language Models with Graph Structure Understanding For Data Sparsity, by Xin Zhang et al.
Fine-grainedly Synthesize Streaming Data Based On Large Language Models With Graph Structure Understanding For Data Sparsity
by Xin Zhang, Linhai Zhang, Deyu Zhou, Guoqiang Xu
First submitted to arxiv on: 10 Mar 2024
Categories
- Main: Computation and Language (cs.CL)
- 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 This paper proposes a fine-grained streaming data synthesis framework for sentiment analysis in e-commerce platforms, addressing the issue of poor performance due to sparse user data or long-tail labels. The approach leverages Large Language Models (LLMs) to generate high-quality synthetic data that can effectively address sparsity across different categories. Specifically, LLMs are designed to understand three key graph elements: local-global graph understanding, second-order relationship extraction, and product attribute understanding. This enables the generation of synthetic data that improves performance in sentiment analysis tasks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps solve a problem with analyzing what people think about products online. When we don’t have much information from users, it’s hard to get accurate results. The authors came up with a new way to create fake data that looks like real user reviews. They use special AI models called Large Language Models to make this fake data. This approach helps improve the accuracy of sentiment analysis tasks by 45-62% compared to previous methods. |
Keywords
» Artificial intelligence » Synthetic data
