Summary of Retrieval-augmented Language Model For Extreme Multi-label Knowledge Graph Link Prediction, by Yu-hsiang Lin et al.
Retrieval-Augmented Language Model for Extreme Multi-Label Knowledge Graph Link Prediction
by Yu-Hsiang Lin, Huang-Ting Shieh, Chih-Yu Liu, Kuang-Ting Lee, Hsiao-Cheng Chang, Jing-Lun Yang, Yu-Sheng Lin
First submitted to arxiv on: 21 May 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 A novel approach to extrapolation in Large Language Models (LLMs) addresses the challenges of hallucination and expensive training costs for open-ended inquiry. Existing methods augment smaller language models with information from knowledge graphs, but fail to extract relevant information or adapt to diverse KG characteristics. The proposed extreme multi-label KG link prediction task enables a model to perform extrapolation with multiple responses using structured real-world knowledge. A retriever identifies relevant one-hop neighbors by combining entity, relation, and textual data. Experiments show that different KGs require tailored augmentation strategies, and augmenting the language model’s input with textual data improves performance significantly. The proposed framework, with a small parameter size, successfully extrapolates based on a given KG. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large Language Models (LLMs) are super smart computers that can understand and generate human-like text. But they have some problems when trying to answer open-ended questions, like hallucination (making things up) and being too expensive to train. Researchers have been working on ways to fix these issues by using special knowledge graphs, but there are still some big challenges. A new task has been proposed that helps LLMs learn from these knowledge graphs in a more effective way. This task is called extreme multi-label KG link prediction, and it’s all about finding the right answers even when there are many possible responses. The researchers used a special tool to help the LLM find the most important information in the knowledge graph, which improved their results. They also found that different types of knowledge graphs require slightly different approaches. |
Keywords
* Artificial intelligence * Hallucination * Knowledge graph * Language model
