Summary of Empowering Few-shot Relation Extraction with the Integration Of Traditional Re Methods and Large Language Models, by Ye Liu et al.
Empowering Few-Shot Relation Extraction with The Integration of Traditional RE Methods and Large Language Models
by Ye Liu, Kai Zhang, Aoran Gan, Linan Yue, Feng Hu, Qi Liu, Enhong Chen
First submitted to arxiv on: 12 Jul 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 Few-Shot Relation Extraction (FSRE) is presented in this paper, which leverages Large Language Models (LLMs) for In-Context Learning (ICL). The authors propose a Dual-System Augmented Relation Extractor (DSARE), combining traditional RE models with LLMs. This hybrid model injects prior knowledge from LLMs into traditional RE models and enhances LLMs’ task-specific capabilities through relation extraction augmentation. A joint prediction module is used to combine the outputs of both systems, resulting in improved performance. The paper demonstrates the effectiveness of DSARE on various datasets and benchmarks. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary FSRE helps computers understand text by extracting information from limited training examples. Researchers use Pre-trained Language Models (PLMs) or Large Language Models (LLMs) for this task. However, these methods have limitations. Traditional models lack prior knowledge, while LLMs struggle with specific tasks like relation extraction. To solve this problem, the authors created a new model called DSARE, which combines traditional and LLM-based approaches. This combination helps both systems learn from each other, leading to better results. |
Keywords
» Artificial intelligence » Few shot
