用论文标题摘要预测5年影响力，助力发现高价值研究方向
MIRAI: Prediction and Generation of High-Impact Academic Research
- 基于标题摘要和发布时间，用深度学习预测论文5年影响力
- 2021年论文预测准确率：引用量相关性0.6192，页面排名0.4686
- 可生成高影响力研究选题，经大模型评估优于传统方法
科学出版速度加快，识别与整合高影响力成果成为紧迫挑战。我们提出MIRAI（多年度研究趋势与学术影响力推断），一种仅需论文标题、摘要和发表日期即可预测其影响力的深度学习框架。在arXiv学术图谱上训练，用于预测5年PageRank与引用次数，对2021年发表论文的预测中，PageRank相关性达Spearman's $ρ$ = 0.4686，引用数预测相关性为0.6192。基于MIRAI构建的研究创意生成流程，能产出高影响力导向的研究想法，经无偏大语言模型评估，其影响力评分优于基线4:3。5年引用预测模型已公开：https://predict-paper-impact.vercel.app。
原文摘要 · Abstract (English)
The rapid pace of scientific publishing has made the identification and synthesis of high-impact work an increasingly urgent challenge. We introduce MIRAI (Multi-year Inference of Research trends and Academic Impact), a deep learning framework that predicts paper impact using only it's title, abstract, and publication date. We train MIRAI on the arXiv academic graph to predict 5-year PageRank and citation counts, achieving Spearman's $ρ$ of 0.4686 on PageRank prediction and 0.6192 on citation prediction for papers published in 2021. We propose a research ideation pipeline built on top of MIRAI that produces research ideas oriented towards high impact. These ideas were judged as more impactful than a baseline without MIRAI by an unbiased LLM judge at a 4:3 ratio. We make the 5-year citation prediction model publicly available at https://predict-paper-impact.vercel.app.
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