Summary of A Few-shot Approach For Relation Extraction Domain Adaptation Using Large Language Models, by Vanni Zavarella and Juan Carlos Gamero-salinas and Sergio Consoli
A Few-Shot Approach for Relation Extraction Domain Adaptation using Large Language Models
by Vanni Zavarella, Juan Carlos Gamero-Salinas, Sergio Consoli
First submitted to arxiv on: 5 Aug 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 paper presents an innovative approach for generating knowledge graphs (KGs) in new scientific domains. Current models are trained on limited datasets, such as SciERC, and struggle to adapt to novel areas. To address this, the authors leverage large language models’ ability to learn from context and use schema-constrained data annotation to collect training instances for a Transformer-based relation extraction model. This approach is tested on titles and abstracts of research papers in the Architecture, Construction, Engineering, and Operations (AECO) domain. The results show that using a few-shot learning strategy with structured prompts and minimal expert annotation can significantly improve performance compared to traditional off-domain training. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps create better systems for understanding complex scientific information. Right now, these systems are only good at one specific area of science. They struggle when they need to learn about something new. To fix this, the authors try using special language models that can learn from what they’ve seen before. They use this model to help another system learn to understand new types of documents in a field called Architecture, Construction, Engineering, and Operations (AECO). By testing this approach, they show it can make their system much better at learning about new areas of science. |
Keywords
» Artificial intelligence » Few shot » Transformer
