Summary of Combining Knowledge Graphs and Large Language Models, by Amanda Kau et al.
Combining Knowledge Graphs and Large Language Models
by Amanda Kau, Xuzeng He, Aishwarya Nambissan, Aland Astudillo, Hui Yin, Amir Aryani
First submitted to arxiv on: 9 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 explores the intersection of Large Language Models (LLMs) and Knowledge Graphs (KGs) in Natural Language Processing (NLP). The authors highlight the limitations of LLMs, such as hallucinations and lack of domain-specific knowledge, which can impact their performance in real-world tasks. To mitigate these issues, they propose incorporating KGs, which organize information in structured formats that capture relationships between entities. This combination has led to a trend combining LLMs and KGs to achieve trustworthy results. The paper presents a comprehensive overview of 28 papers outlining methods for KG-powered LLMs, LLM-based KGs, and LLM-KG hybrid approaches. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human language. But they’re not perfect – sometimes they make things up or don’t know about specific topics. To help them be more accurate, scientists propose combining them with Knowledge Graphs (KGs). KGs are like super organized dictionaries that show how different ideas are connected. When you put LLMs and KGs together, it’s like having a super smart librarian who can find the right information quickly. This paper looks at 28 different ways researchers have combined LLMs and KGs to make them work better together. |
Keywords
* Artificial intelligence * Natural language processing * Nlp
