Summary of Data-centric Ai in the Age Of Large Language Models, by Xinyi Xu et al.
Data-Centric AI in the Age of Large Language Models
by Xinyi Xu, Zhaoxuan Wu, Rui Qiao, Arun Verma, Yao Shu, Jingtan Wang, Xinyuan Niu, Zhenfeng He, Jiangwei Chen, Zijian Zhou, Gregory Kang Ruey Lau, Hieu Dao, Lucas Agussurja, Rachael Hwee Ling Sim, Xiaoqiang Lin, Wenyang Hu, Zhongxiang Dai, Pang Wei Koh, Bryan Kian Hsiang Low
First submitted to arxiv on: 20 Jun 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Computation and Language (cs.CL)
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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 position paper proposes a data-centric viewpoint on AI research, focusing on large language models (LLMs). The authors highlight the crucial role of data in both developmental and inferential stages of LLMs. They identify four specific scenarios centered around data, including data-centric benchmarks, curation, attribution, knowledge transfer, and inference contextualization. These scenarios demonstrate the importance of data, promising research directions, and potential impacts on the community and society. For instance, they advocate for a suite of data-centric benchmarks tailored to LLMs’ scale and complexity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper is about how important data is in artificial intelligence research. It’s all about big language models that can understand human language. The authors think that data is really underappreciated when it comes to these models, so they’re proposing new ways of working with data. They want to create special benchmarks to test how well these models do, and make it easier for researchers to share their work. This could help make AI research more transparent and open. |
Keywords
» Artificial intelligence » Inference
