自动化风险投资：使用LLM驱动的细分、特征工程和自动化标注技术进行创始人评估
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
AI总结：
本研究提出利用LLM提示与机器学习从创始人特征预测初创企业成功，并验证该框架对风险投资决策优化的价值。
摘要：
本研究探讨了大型语言模型（LLMs）在风险投资（VC）决策中的应用，重点关注基于创始人特征来预测初创企业成功。我们利用LLM提示技术（如思维链）从有限数据中生成特征，然后通过统计和机器学习提取洞见。我们的结果揭示了某些创始人特征与成功之间的潜在关系，并证明了这些特征在预测中的有效性。这一整合机器学习技术与LLMs的框架在改进初创企业成功预测方面具有巨大潜力，对寻求优化投资策略的风险投资公司具有重要意义。
英文摘要：
This study explores the application of large language models (LLMs) in venture capital (VC) decision-making, focusing on predicting startup success based on founder characteristics. We utilize LLM prompting techniques, like chain-of-thought, to generate features from limited data, then extract insights through statistics and machine learning. Our results reveal potential relationships between certain founder characteristics and success, as well as demonstrate the effectiveness of these characteristics in prediction. This framework for integrating ML techniques and LLMs has vast potential for improving startup success prediction, with important implications for VC firms seeking to optimize their investment strategies.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
