让用户互动定制论文综述，一键生成高质量多模态综述。
InteractiveSurvey: An LLM-based Personalized and Interactive Survey Paper Generation System
- 基于大模型与检索增强生成，支持上传和在线检索参考文献。
- 可交互调整参考文献分类、大纲与内容，支持个性化迭代优化。
- 实测生成质量优于主流模型，且大幅节省撰写时间。
学术文献的爆炸式增长带来了对综述论文的迫切需求，但人工撰写耗时费力。尽管大语言模型（LLMs）和检索增强生成（RAG）技术已助力从多篇参考文献中合成综述，但现有方法大多仅支持标题输入和固定输出，忽视了综述撰写过程的个性化需求。本文提出 InteractiveSurvey——一个基于大模型的个性化、交互式综述论文生成系统。该系统可从多篇参考文献（包括在线检索与用户上传）中生成结构化、多模态的综述论文，并自动进行参考文献分类。更重要的是，用户可通过直观界面持续自定义和优化中间步骤，如参考文献分类、文章大纲及内容生成。在内容质量、时间效率及用户研究方面均显示，InteractiveSurvey 是一个易用性高、生成质量优且高效的方法，显著优于多数主流大模型与现有方法。
原文摘要 · Abstract (English)
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.
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