Summary of Large Language Models Can Better Understand Knowledge Graphs Than We Thought, by Xinbang Dai et al.
Large Language Models Can Better Understand Knowledge Graphs Than We Thought
by Xinbang Dai, Yuncheng Hua, Tongtong Wu, Yang Sheng, Qiu Ji, Guilin Qi
First submitted to arxiv on: 18 Feb 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 research paper explores the effectiveness of incorporating factual knowledge from knowledge graphs into large language models. The study finds that the cost of injecting this information into the models increases with their scale, making it crucial to develop prompt strategies that efficiently incorporate KG information. The authors design experiments to understand how LLMs process and interpret KG information in different input formats and organizations within prompts. They reveal LLMs’ preferences for various input formats, from linearized triples to fluent natural language text, and discuss the underlying mechanisms driving these preferences. The study also investigates how the organization of structured knowledge impacts LLMs and evaluates their robustness in processing and utilizing KG information. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models can now understand and answer fact-intensive questions better by incorporating factual knowledge from knowledge graphs! Researchers wanted to see how well this works, so they did some cool experiments. They found that using linearized triples (like a list of facts) is better than using natural language text for helping LLMs learn KG information. They also discovered that different models like Google’s BERT or RoBERTa have different preferences for organizing this knowledge. Plus, bigger models are more sensitive to noisy or incomplete data. |
Keywords
» Artificial intelligence » Bert » Prompt
