Summary of Generating Knowledge Graphs From Large Language Models: a Comparative Study Of Gpt-4, Llama 2, and Bert, by Ahan Bhatt et al.
Generating Knowledge Graphs from Large Language Models: A Comparative Study of GPT-4, LLaMA 2, and BERT
by Ahan Bhatt, Nandan Vaghela, Kush Dudhia
First submitted to arxiv on: 10 Dec 2024
Categories
- Main: Computation and Language (cs.CL)
- Secondary: Artificial Intelligence (cs.AI); Databases (cs.DB)
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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 A novel approach leverages large language models (LLMs) like GPT-4, LLaMA 2 (13B), and BERT to directly generate Knowledge Graphs (KGs) from unstructured data, bypassing traditional pipelines. The paper evaluates the models’ ability to generate high-quality KGs using metrics such as Precision, Recall, F1-Score, Graph Edit Distance, and Semantic Similarity. GPT-4 achieves superior semantic fidelity and structural accuracy, LLaMA 2 excels in lightweight, domain-specific graphs, and BERT provides insights into challenges in entity-relationship modeling. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Knowledge Graphs are important for a type of computer system that does well with tasks that need structured thinking and understanding. Making these graphs is hard because traditional methods aren’t very good at getting the right information quickly. This paper shows how to use large language models like GPT-4, LLaMA 2, and BERT to create Knowledge Graphs directly from unstructured data. This makes it easier to make these computer systems work better. |
Keywords
» Artificial intelligence » Bert » F1 score » Gpt » Llama » Precision » Recall
