Summary of Unifying Large Language Models and Knowledge Graphs: a Roadmap, by Shirui Pan et al.
Unifying Large Language Models and Knowledge Graphs: A Roadmap
by Shirui Pan, Linhao Luo, Yufei Wang, Chen Chen, Jiapu Wang, Xindong Wu
First submitted to arxiv on: 14 Jun 2023
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 abstract proposes a unification of large language models (LLMs) like ChatGPT and GPT4 with Knowledge Graphs (KGs), such as Wikipedia, to leverage the strengths of both. LLMs are black-box models that struggle to capture factual knowledge, while KGs store rich factual knowledge but are difficult to construct and evolve. The unification aims to integrate LLMs’ generalizability and KGs’ factual knowledge for enhanced inference, interpretability, and bidirectional reasoning. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary The paper presents a roadmap for combining LLMs and KGs in three frameworks: 1) KG-enhanced LLMs, which incorporate KGs during pre-training and inference; 2) LLM-augmented KGs, leveraging LLMs for KG tasks like embedding and question answering; and 3) Synergized LLMs + KGs, where both play equal roles to enhance each other. The roadmap reviews existing efforts and outlines future research directions within these frameworks. |
Keywords
* Artificial intelligence * Embedding * Inference * Question answering
