@inproceedings{xu-etal-2024-position,
    title = "Position Paper: Data-Centric {AI} in the Age of Large Language Models",
    author = "Xu, Xinyi  and
      Wu, Zhaoxuan  and
      Qiao, Rui  and
      Verma, Arun  and
      Shu, Yao  and
      Wang, Jingtan  and
      Niu, Xinyuan  and
      He, Zhenfeng  and
      Chen, Jiangwei  and
      Zhou, Zijian  and
      Lau, Gregory Kang Ruey  and
      Dao, Hieu  and
      Agussurja, Lucas  and
      Sim, Rachael Hwee Ling  and
      Lin, Xiaoqiang  and
      Hu, Wenyang  and
      Dai, Zhongxiang  and
      Koh, Pang Wei  and
      Low, Bryan Kian Hsiang",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2024",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.findings-emnlp.695",
    doi = "10.18653/v1/2024.findings-emnlp.695",
    pages = "11895--11913",
    abstract = "This position paper proposes a data-centric viewpoint of AI research, focusing on large language models (LLMs). We start by making a key observation that data is instrumental in the developmental (e.g., pretraining and fine-tuning) and inferential stages (e.g., in-context learning) of LLMs, and advocate that data-centric research should receive more attention from the community. We identify four specific scenarios centered around data, covering data-centric benchmarks and data curation, data attribution, knowledge transfer, and inference contextualization. In each scenario, we underscore the importance of data, highlight promising research directions, and articulate the potential impacts on the research community and, where applicable, the society as a whole. For instance, we advocate for a suite of data-centric benchmarks tailored to the scale and complexity of data for LLMs. These benchmarks can be used to develop new data curation methods and document research efforts and results, which can help promote openness and transparency in AI and LLM research.",
}