Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
<|reference_start|>Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance: As scientific research proliferates, researchers face the daunting task of navigating and reading vast amounts of literature. Existing solutions, such as document QA, fail to provide personalized and up-to-date information efficiently. We present Paper Copilot, a self-evolving, efficient LLM system designed to assist researchers, based on thought-retrieval, user profile and high performance optimization. Specifically, Paper Copilot can offer personalized research services, maintaining a real-time updated database. Quantitative evaluation demonstrates that Paper Copilot saves 69.92\% of time after efficient deployment. This paper details the design and implementation of Paper Copilot, highlighting its contributions to personalized academic support and its potential to streamline the research process.<|reference_end|>
@article{lin2024paper,
    title={Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized
  Academic Assistance},
    author={Guanyu Lin, Tao Feng, Pengrui Han, Ge Liu, Jiaxuan You},
    journal={arXiv preprint arXiv:2409.04593},
    year={2024},
    archivePrefix={arXiv},
    eprint={2409.04593},
    primaryClass={cs.CL}
}