Arxiv Copilot: A Self-Evolving and Efficient LLM System for Personalized Academic Assistance
Guanyu Lin, Tao Feng, Pengrui Han, Ge Liu, Jiaxuan You
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 Arxiv Copilot, a self-evolving, efficient LLM system designed to assist researchers, based on thought-retrieval, user profile and high performance optimization. Specifically, Arxiv Copilot can offer personalized research services, maintaining a real-time updated database. Quantitative evaluation demonstrates that Arxiv Copilot saves 69.92% of time after efficient deployment. This paper details the design and implementation of Arxiv Copilot, highlighting its contributions to personalized academic support and its potential to streamline the research process. We have deployed Arxiv Copilot at: https://huggingface.co/spaces/ulab-ai/ArxivCopilot.
BibTeX
@inproceedings{lin-etal-2024-arxiv,
title = "{A}rxiv Copilot: A Self-Evolving and Efficient {LLM} System for Personalized Academic Assistance",
author = "Lin, Guanyu and
Feng, Tao and
Han, Pengrui and
Liu, Ge and
You, Jiaxuan",
editor = "Hernandez Farias, Delia Irazu and
Hope, Tom and
Li, Manling",
booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: System Demonstrations",
month = nov,
year = "2024",
address = "Miami, Florida, USA",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2024.emnlp-demo.13/",
doi = "10.18653/v1/2024.emnlp-demo.13",
pages = "122--130"
}