Summary of Knowledge Bases in Support Of Large Language Models For Processing Web News, by Yihe Zhang et al.
Knowledge Bases in Support of Large Language Models for Processing Web News
by Yihe Zhang, Nabin Pakka, Nian-Feng Tzeng
First submitted to arxiv on: 13 Nov 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 The paper introduces a framework that leverages Large Language Models (LLMs) to build knowledge bases for processing Web news. The framework consists of two lightweight components: News Information Extractor (NewsIE) and BERTGraph. NewsIE extracts structural information from news items in the form of relational tuples, while BERTGraph graph-convolutes implicit knowledge facts with these tuples for classification. The paper evaluates this framework on various news-related datasets for news category classification, achieving promising results. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses special computers to help make sense of a lot of online news articles. It’s like creating a big dictionary of what each article is saying about the world. Then, it connects all these dictionary entries together so they can work better with each other. The goal is to figure out which type of news article something is, like whether it’s a sports story or a politics story. The results look good, and this could be useful for people who want to keep up with the latest news. |
Keywords
» Artificial intelligence » Classification
