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基于大语言模型的科学文献实体解析的可视化分析
原标题：Visual Analysis of LLM-based Entity Resolution from Scientific Papers
AI 摘要
该论文提出了一种结合大语言模型（如GPT-4）与可视化分析的人机协同实体解析流程，用于从科学文献中提取领域特定实体，并以金属有机框架（MOFs）材料领域为例进行验证。通过交互式错误分析和RAG过程解释，该方法将单文档实体解析准确率提升了约30%。研究强调了可视化在提升LLM实体解析可解释性和准确性方面的作用。
以上摘要由 AI 生成，可能存在误差。事实请以原文为准。
正文节选
Visual Analysis of LLM-based Entity Resolution from Scientific Papers Abstract This paper focuses on the visual analytics support for extracting domain-specific entity from extensive scientific literature, a task with inherent limitations using traditional named entity resolution methods. With the advent of large language models (LLMs) such as GPT-4, significant improvements over conventional machine learning approaches have been achieved due to LLM’s capability on entity resolution integrate ab
发布时间：2026-09-09 12:00
抓取时间：2026-09-09 12:47
来源机构：arXiv