Summary of Data Augmentation Using Large Language Models: Data Perspectives, Learning Paradigms and Challenges, by Bosheng Ding et al.
Data Augmentation using Large Language Models: Data Perspectives, Learning Paradigms and Challenges
by Bosheng Ding, Chengwei Qin, Ruochen Zhao, Tianze Luo, Xinze Li, Guizhen Chen, Wenhan Xia, Junjie Hu, Anh Tuan Luu, Shafiq Joty
First submitted to arxiv on: 5 Mar 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 The survey explores the impact of large language models (LLMs) on data augmentation (DA) in natural language processing (NLP) and beyond. It examines various strategies that utilize LLMs for DA, including novel explorations of learning paradigms where LLM-generated data is used for diverse forms of further training. The paper highlights the primary open challenges faced in this domain, ranging from controllable data augmentation to multi-modal data augmentation. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Large language models are changing how we approach data augmentation. This survey looks at how these models can help us come up with new ways to train our machines. It shows that LLMs can generate new training examples without needing more data. This is a big deal because it makes our machines better and faster. |
Keywords
» Artificial intelligence » Data augmentation » Multi modal » Natural language processing » Nlp
