論文Copilot：一個自我演化且高效的LLM系統，用於個性化學術輔助
Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
September 6, 2024
作者: Guanyu Lin, Tao Feng, Pengrui Han, Ge Liu, Jiaxuan You
cs.AI
摘要
隨著科學研究的蓬勃發展，研究人員面臨著艱鉅的任務，需要導航和閱讀龐大的文獻。現有的解決方案，如文件問答系統，未能有效提供個性化和最新信息。我們提出了Paper Copilot，這是一個自我演進、高效的LLM系統，旨在基於思維檢索、用戶檔案和高性能優化來協助研究人員。具體而言，Paper Copilot能夠提供個性化的研究服務，並保持實時更新的數據庫。定量評估顯示，Paper Copilot在高效部署後節省了69.92%的時間。本文詳細介紹了Paper Copilot的設計和實施，突出了其對個性化學術支持的貢獻以及簡化研究過程的潛力。
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.