Summary of A Survey Of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications, by Wenbo Shang et al.
A Survey of Large Language Models on Generative Graph Analytics: Query, Learning, and Applications
by Wenbo Shang, Xin Huang
First submitted to arxiv on: 23 Apr 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 Large language models (LLMs) have demonstrated remarkable generalization abilities in handling various natural language processing (NLP) and multi-modal tasks. In contrast, traditional graph learning models require manual annotation and training, making LLMs a more cost-effective solution for addressing complex graph-related challenges. This survey comprehensively investigates existing LLM studies on graph data, highlighting the key problems and future directions of generative graph analytics (GGA). The study categorizes GGA into three areas: query processing (LLM-GQP), inference and learning (LLM-GIL), and applications. LLM-GQP integrates graph analytics techniques with LLM prompts for enhanced graph understanding and knowledge graph-based augmented retrieval, while LLM-GIL focuses on learning and reasoning over graphs through graph learning, formed reasoning, and representation. The survey also provides an overview of useful LLM prompts for handling different graph downstream tasks, model evaluation metrics, benchmark datasets and tasks, as well as a deep analysis of the pros and cons of LLM models. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Imagine trying to understand very complex networks like social media or financial systems. Researchers have been working on special computer programs called large language models (LLMs) that can help us make sense of these networks. Unlike other methods, LLMs don’t need as much training data and are more efficient. This study looks at how LLMs can be used to analyze these complex networks. The researchers identified three main areas where LLMs can help: processing queries, making predictions, and applying the results. They also talked about what makes LLMs good or bad for this kind of work. Overall, this research is important because it shows us how we can use powerful computer programs to better understand and manage our complex networks. |
Keywords
» Artificial intelligence » Generalization » Inference » Knowledge graph » Multi modal » Natural language processing » Nlp
