Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance

Ge Liu; Guanyu Lin; Jiaxuan You; Pengrui Han; Tao Feng

Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance

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arxiv 2409.04593 v1 pith:67NJD776 submitted 2024-09-06 cs.CL

Guanyu Lin , Tao Feng , Pengrui Han , Ge Liu , Jiaxuan You This is my paper · ORCID

classification cs.CL

keywords copilotpersonalizedefficientresearchacademicresearchersself-evolvingsystem

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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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