用大模型从零数据中挖掘关键特征，预测风投中的稀有高回报项目。
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
- 用大模型从非结构化数据中提取复杂信号，构建可解释的特征工程
- 在三个测试集上，精确率比随机基线高出9.8到11.1倍
- 揭示了创业领域、创始人数量等关键影响因素，适合风投决策者使用
本文提出一种框架，通过整合大语言模型（LLMs）与多模型机器学习架构，实现对罕见但高影响结果的预测。该方法结合黑箱模型的预测力与可解释性需求，利用大模型驱动的特征工程从非结构化数据中提取并合成复杂信号，并在包含XGBoost、随机森林和线性回归的分层集成模型中处理。集成模型先输出连续的成功概率估计，再经阈值化生成二分类的稀有事件预测。该框架应用于风投领域，应对早期数据有限且嘈杂的问题。实证结果显示：在三个独立测试子集上，模型精度达随机基线的9.8至11.1倍。特征敏感性分析进一步揭示：创业公司所属类别贡献15.6%的预测影响力，其次是创始人数量，教育水平和领域经验也表现出小但稳定的正向影响。
原文摘要 · Abstract (English)
This paper presents a framework for predicting rare, high-impact outcomes by integrating large language models (LLMs) with a multi-model machine learning (ML) architecture. The approach combines the predictive strength of black-box models with the interpretability required for reliable decision-making. We use LLM-powered feature engineering to extract and synthesize complex signals from unstructured data, which are then processed within a layered ensemble of models including XGBoost, Random Forest, and Linear Regression. The ensemble first produces a continuous estimate of success likelihood, which is then thresholded to produce a binary rare-event prediction. We apply this framework to the domain of Venture Capital (VC), where investors must evaluate startups with limited and noisy early-stage data. The empirical results show strong performance: the model achieves precision between 9.8X and 11.1X the random classifier baseline in three independent test subsets. Feature sensitivity analysis further reveals interpretable success drivers: the startup's category list accounts for 15.6% of predictive influence, followed by the number of founders, while education level and domain expertise contribute smaller yet consistent effects.
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