Paper Copilot用自进化大模型帮研究人员高效查文献、省时间。
Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
- 基于思想检索与用户画像，动态更新研究数据库。
- 部署后节省69.92%文献阅读时间。
- 适合需要个性化学术支持的科研人员。
随着科研文献激增，研究人员面临海量文献筛选与阅读的挑战。现有方案如文档问答无法高效提供个性化且实时的信息。本文提出 Paper Copilot，一个自进化、高效的 LLM 系统，基于思想检索、用户画像与高性能优化，为研究者提供个性化服务，并维持实时更新的数据库。定量评估显示，该系统在高效部署后可节省69.92%的文献阅读时间。本文详细阐述了 Paper Copilot 的设计与实现，强调其在个性化学术支持方面的贡献及对研究流程的优化潜力。
原文摘要 · Abstract (English)
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.
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