用AI分析论文创新性演进，揭示研究脉络与空白
Mapping the Evolution of Research Contributions using KnoVo
- 通过LLM提取方法、数据等维度，动态比较论文前后创新度
- 生成量化新颖性评分，展现研究在各维度的进展或滞后
- 适合追踪领域演进、找研究缺口，尤其跨学科探索者
本文提出KnoVo（知识演化）框架，用于量化和分析科学文献中研究创新性的演变。不同于传统以引用量衡量影响力的模式，KnoVo基于多层引用网络，评估目标论文相对于前序与后续工作的创新性。给定目标论文摘要，该框架利用大语言模型（LLMs）动态提取对比维度（如方法、应用、数据集），并将目标论文与相关文献在这些维度上进行比较。这种受锦标赛选择启发的分析，生成反映目标论文在特定方面相对改进、等同或劣化的定量新颖性评分。通过聚合这些评分并可视化其演变过程（如动态演进图、对比雷达图），KnoVo帮助研究人员不仅评估原创性、识别相似工作，还能追踪特定研究维度的知识演进，发现研究空白，并探索跨学科关联。我们通过对20篇来自多个科学领域的多样化论文进行详细分析，报告了多种开源LLMs在KnoVo框架中的表现。
原文摘要 · Abstract (English)
This paper presents KnoVo (Knowledge Evolution), an intelligent framework designed for quantifying and analyzing the evolution of research novelty in the scientific literature. Moving beyond traditional citation analysis, which primarily measures impact, KnoVo determines a paper's novelty relative to both prior and subsequent work within its multilayered citation network. Given a target paper's abstract, KnoVo utilizes Large Language Models (LLMs) to dynamically extract dimensions of comparison (e.g., methodology, application, dataset). The target paper is then compared to related publications along these same extracted dimensions. This comparative analysis, inspired by tournament selection, yields quantitative novelty scores reflecting the relative improvement, equivalence, or inferiority of the target paper in specific aspects. By aggregating these scores and visualizing their progression, for instance, through dynamic evolution graphs and comparative radar charts, KnoVo facilitates researchers not only to assess originality and identify similar work, but also to track knowledge evolution along specific research dimensions, uncover research gaps, and explore cross-disciplinary connections. We demonstrate these capabilities through a detailed analysis of 20 diverse papers from multiple scientific fields and report on the performance of various open-source LLMs within the KnoVo framework.
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。