Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
Guanyu Lin, Tao Feng, Pengrui Han, Ge Liu, Jiaxuan You
2024-09-10
Summary
This paper talks about Paper Copilot, an advanced AI system designed to help researchers efficiently navigate and understand vast amounts of academic literature.
What's the problem?
Researchers often struggle to keep up with the growing amount of scientific papers and information available. Current tools, like document question-answering systems, do not provide personalized or timely information effectively, making it hard for researchers to find relevant insights quickly.
What's the solution?
Paper Copilot addresses this issue by using a self-evolving system that continuously updates its database with the latest research. It personalizes the assistance it provides based on the user's profile and needs. The system has been shown to save researchers nearly 70% of their time when looking for information, making the research process much more efficient.
Why it matters?
This research is important because it offers a solution to a common problem faced by many researchers today. By streamlining how they access and understand academic literature, Paper Copilot can significantly enhance productivity and help researchers stay current in their fields.
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.