@inproceedings{wu-etal-2024-sparkra,
    title = "{S}park{RA}: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model",
    author = "Wu, Dayong  and
      Li, Jiaqi  and
      Wang, Baoxin  and
      Zhao, Honghong  and
      Xue, Siyuan  and
      Yang, Yanjie  and
      Chang, Zhijun  and
      Zhang, Rui  and
      Qian, Li  and
      Wang, Bo  and
      Wang, Shijin  and
      Zhang, Zhixiong  and
      Hu, Guoping",
    editor = "Hernandez Farias, Delia Irazu  and
      Hope, Tom  and
      Li, Manling",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/jlcl-multiple-ingestion/2024.emnlp-demo.40/",
    doi = "10.18653/v1/2024.emnlp-demo.40",
    pages = "382--389",
    abstract = "Large language models (LLMs) have shown remarkable achievements across various language tasks. To enhance the performance of LLMs in scientific literature services, we developed the scientific literature LLM (SciLit-LLM) through pre-training and supervised fine-tuning on scientific literature, building upon the iFLYTEK Spark LLM. Furthermore, we present a knowledge service system Spark Research Assistant (SparkRA) based on our SciLit-LLM. SparkRA is accessible online and provides three primary functions: literature investigation, paper reading, and academic writing. As of July 30, 2024, SparkRA has garnered over 50,000 registered users, with a total usage count exceeding 1.3 million."
}