The exponential growth of academic literature creates urgent demands for comprehensive survey papers, yet manual writing remains time-consuming and labor-intensive. Recent advances in large language models (LLMs) and retrieval-augmented generation (RAG) facilitate studies in synthesizing survey papers from multiple references, but most existing works restrict users to title-only inputs and fixed outputs, neglecting the personalized process of survey paper writing. In this paper, we introduce InteractiveSurvey - an LLM-based personalized and interactive survey paper generation system. InteractiveSurvey can generate structured, multi-modal survey papers with reference categorizations from multiple reference papers through both online retrieval and user uploads. More importantly, users can customize and refine intermediate components continuously during generation, including reference categorization, outline, and survey content through an intuitive interface. Evaluations of content quality, time efficiency, and user studies show that InteractiveSurvey is an easy-to-use survey generation system that outperforms most LLMs and existing methods in output content quality while remaining highly time-efficient.
View on arXiv@article{wen2025_2504.08762,
title={ InteractiveSurvey: An LLM-based Personalized and Interactive Survey Paper Generation System },
author={ Zhiyuan Wen and Jiannong Cao and Zian Wang and Beichen Guo and Ruosong Yang and Shuaiqi Liu },
journal={arXiv preprint arXiv:2504.08762},
year={ 2025 }
}