用大模型和多智能体自动完成系统综述，省时省力。
LatteReview: A Multi-Agent Framework for Systematic Review Automation Using Large Language Models
- 设计多智能体系统分步处理文献筛选、评估与数据提取。
- 支持迭代优化和用户反馈，可处理大规模文献数据。
- 适合科研人员快速开展高质量系统综述，提升效率。
系统性文献综述和元分析对整合研究见解至关重要，但因其筛选、评估和数据提取的迭代过程，仍耗时耗力。本文提出并评估了LatteReview，一个基于Python的框架，利用大语言模型（LLMs）和多智能体系统自动化系统综述的关键环节。该框架通过模块化智能体实现标题摘要筛选、相关性评分和结构化数据提取，支持顺序与并行审查轮次、动态决策及基于用户反馈的迭代优化。其架构集成多种LLM提供方，兼容云端与本地部署模型，支持检索增强生成（RAG）以引入外部上下文、多模态综述、Pydantic验证结构化输入输出，以及异步编程处理大规模数据集。框架已开源至GitHub，提供详细文档与可安装包。
原文摘要 · Abstract (English)
Systematic literature reviews and meta-analyses are essential for synthesizing research insights, but they remain time-intensive and labor-intensive due to the iterative processes of screening, evaluation, and data extraction. This paper introduces and evaluates LatteReview, a Python-based framework that leverages large language models (LLMs) and multi-agent systems to automate key elements of the systematic review process. Designed to streamline workflows while maintaining rigor, LatteReview utilizes modular agents for tasks such as title and abstract screening, relevance scoring, and structured data extraction. These agents operate within orchestrated workflows, supporting sequential and parallel review rounds, dynamic decision-making, and iterative refinement based on user feedback. LatteReview's architecture integrates LLM providers, enabling compatibility with both cloud-based and locally hosted models. The framework supports features such as Retrieval-Augmented Generation (RAG) for incorporating external context, multimodal reviews, Pydantic-based validation for structured inputs and outputs, and asynchronous programming for handling large-scale datasets. The framework is available on the GitHub repository, with detailed documentation and an installable package.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。