为提升LLM辅助文献综述的可信度与协作效率，提出六项设计目标。
From Verification Burden to Trusted Collaboration: Design Goals for LLM-Assisted Literature Reviews
- 基于跨学科用户研究，提炼出信任缺失、验证负担重、工具分散三大痛点。
- 提出可视化相关论文、每步可验证、人机反馈对齐等机制提升可信度。
- 适合关注AI写作工具落地、提升科研协作效率的研究者参考。
大型语言模型（LLMs）正日益融入学术写作实践。尽管已有大量研究探讨研究人员如何使用这些工具进行科学写作，但其在文献综述过程中的具体应用、局限性及设计挑战仍缺乏深入探索。本文通过跨学科用户研究，分析了研究人员在查阅相关工作时的实际做法、收益与痛点。发现三个反复出现的缺口：（i）对输出结果缺乏信任，（ii）持续存在验证负担，（iii）需使用多个工具。据此，我们提出六项设计目标，并构建一个高层级框架，通过改进相关论文可视化、每一步的验证机制以及生成引导下的反馈对齐来实现这些目标。整体而言，本工作立足于研究人员日常需求，设计了一个能应对上述局限、模拟真实世界中人机协作的框架，以可验证行为增强信任，推动研究者与AI系统之间的务实合作。
原文摘要 · Abstract (English)
Large Language Models (LLMs) are increasingly embedded in academic writing practices. Although numerous studies have explored how researchers employ these tools for scientific writing, their concrete implementation, limitations, and design challenges within the literature review process remain underexplored. In this paper, we report a user study with researchers across multiple disciplines to characterize current practices, benefits, and \textit{pain points} in using LLMs to investigate related work. We identified three recurring gaps: (i) lack of trust in outputs, (ii) persistent verification burden, and (iii) requiring multiple tools. This motivates our proposal of six design goals and a high-level framework that operationalizes them through improved related papers visualization, verification at every step, and human-feedback alignment with generation-guided explanations. Overall, by grounding our work in the practical, day-to-day needs of researchers, we designed a framework that addresses these limitations and models real-world LLM-assisted writing, advancing trust through verifiable actions and fostering practical collaboration between researchers and AI systems.
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。