Paper Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
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.
Community
This is an automated message from the Librarian Bot. I found the following papers similar to this paper.
The following papers were recommended by the Semantic Scholar API
- SparkRA: A Retrieval-Augmented Knowledge Service System Based on Spark Large Language Model (2024)
- LLM Inference Serving: Survey of Recent Advances and Opportunities (2024)
- Meta Knowledge for Retrieval Augmented Large Language Models (2024)
- Retrieval Augmented Generation or Long-Context LLMs? A Comprehensive Study and Hybrid Approach (2024)
- A General-Purpose Device for Interaction with LLMs (2024)
Please give a thumbs up to this comment if you found it helpful!
If you want recommendations for any Paper on Hugging Face checkout this Space
You can directly ask Librarian Bot for paper recommendations by tagging it in a comment:
@librarian-bot
recommend
Models citing this paper 0
No model linking this paper