用生成式AI高效完成文献综述，提升研究效率与质量
Generative Artificial Intelligence for Literature Reviews
- 结合通用与专用GenAI工具，设计可复现的文献综述流程
- 支持大规模文本摘要、问答与数据提取，显著降低人工成本
- 适合科研人员、学生及需要快速掌握领域动态者使用
基于大语言模型（LLMs）的生成式人工智能（GenAI），如ChatGPT、Gemini、Claude等，已广泛应用于学术界与公众领域。其在长文本摘要、问答、数据提取与翻译等方面的强大能力，深刻影响文献综述的开展方式。本文基于技术基础与方法论框架，系统介绍如何利用通用型（如ChatGPT）与专用型（如Consensus、Elicit）GenAI工具进行文献综述，提供具体提示词示例与方法论建议。文章采用平衡视角，既探讨GenAI带来的机遇，也关注其潜在风险。最后，讨论了GenAI对长期科学进步的哲学影响，并提出改进模型架构与训练数据的研究方向，以及当前方法论中尚未解决的关键问题。
原文摘要 · Abstract (English)
Generative artificial intelligence (GenAI), based on large-language models (LLMs), such as ChatGPT, has taken organizations, academia, and the public by storm. In particular, impressive GenAI capabilities such as summarization of large text corpora, question-answering, data extraction, and translation, carry profound implications for the conduct of literature reviews. This impacts science, organizations and the general public, as all can benefit from GenAI-supported literature reviews. Building on the technical foundations of GenAI and grounded in established methodological discourse, this work outlines approaches for conducting literature reviews using both general-purpose (e.g., ChatGPT, Gemini, Claude) and specialized GenAI tools (e.g., Consensus, Elicit). We provide illustrative examples of prompts and suggest methodologically-sound literature review strategies. Throughout this perspective paper, we adopt a balanced approach considering both the opportunities and the risks of relying on GenAI in the conduct of literature reviews. We conclude by discussing philosophical questions related to the effects of GenAI on long-term scientific progress, and also present fruitful opportunities for research on improving the core of GenAI's technology-its architecture and training data-and suggest open issues in GenAI-based literature reviews methodology.
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