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arXiv · 2409.04593

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

Guanyu Lin·Tao Feng·Pengrui Han·Ge Liu·Jiaxuan You
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Abstract

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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Guanyu Lin, Tao Feng, Pengrui Han, Ge Liu, Jiaxuan You. 2024-09-06. Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance. https://arxiv.org/abs/2409.04593

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