用融合大模型分析创业公司自述文本，预测成功率。
A Fused Large Language Model for Predicting Startup Success
- 融合文本与结构化数据的专用大模型
- 在20172个初创企业数据上准确预测成功
- 适合风险投资决策者快速筛选潜力项目
投资者持续寻求初创企业的盈利机会，为有效决策，需预测初创企业的成功概率。如今，投资者不仅可获取初创企业的基础信息（如成立年限、创始人数量、行业领域），还能利用在线风投平台（如Crunchbase）上的创新与商业模式文本描述。为支持投资者决策，本文开发了一种机器学习方法，旨在从风投平台中识别出有潜力的初创企业。具体而言，我们构建、训练并评估了一个定制化的融合大型语言模型，用于预测初创企业成功。通过分析来自Crunchbase的20,172个在线资料，我们发现该融合大模型能有效预测初创企业成功，其中文本自我描述贡献了显著的预测能力。本研究为投资者提供了一种决策支持工具，以发现高回报的投资机会。
原文摘要 · Abstract (English)
Investors are continuously seeking profitable investment opportunities in startups and, hence, for effective decision-making, need to predict a startup's probability of success. Nowadays, investors can use not only various fundamental information about a startup (e.g., the age of the startup, the number of founders, and the business sector) but also textual description of a startup's innovation and business model, which is widely available through online venture capital (VC) platforms such as Crunchbase. To support the decision-making of investors, we develop a machine learning approach with the aim of locating successful startups on VC platforms. Specifically, we develop, train, and evaluate a tailored, fused large language model to predict startup success. Thereby, we assess to what extent self-descriptions on VC platforms are predictive of startup success. Using 20,172 online profiles from Crunchbase, we find that our fused large language model can predict startup success, with textual self-descriptions being responsible for a significant part of the predictive power. Our work provides a decision support tool for investors to find profitable investment opportunities.
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