Summary of Large Language Model Enhanced Knowledge Representation Learning: a Survey, by Xin Wang et al.
Large Language Model Enhanced Knowledge Representation Learning: A Survey
by Xin Wang, Zirui Chen, Haofen Wang, Leong Hou U, Zhao Li, Wenbin Guo
First submitted to arxiv on: 1 Jul 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 focuses on enhancing Knowledge Representation Learning (KRL) for Knowledge Graphs (KGs) by incorporating textual information from Large Language Models (LLMs). The authors propose three LLM-enhanced KRL methods, including encoder-based, encoder-decoder-based, and decoder-based approaches. These methods leverage contextual information to address the sparseness of KGs and improve the effectiveness and generalization of KRL in various downstream tasks. By incorporating textual information from LLMs, these methods can better model KG structural information and enable applications such as question answering, text classification, and named entity recognition. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about using computers to understand and work with big collections of knowledge, called Knowledge Graphs (KGs). The problem is that these KGs are very sparse, meaning they don’t have enough information. To fix this, the authors combine these KGs with Large Language Models (LLMs), which are good at understanding language. They propose three ways to do this: one way uses a special kind of computer model to understand context, another way uses a sequence-to-sequence model to encode and decode knowledge, and the third way uses a big corpus of text to learn from. These methods can help computers better understand KGs and apply that understanding to tasks like answering questions, classifying text, and recognizing named entities. |
Keywords
» Artificial intelligence » Decoder » Encoder » Encoder decoder » Generalization » Named entity recognition » Question answering » Representation learning » Sequence model » Text classification
