用少量成功案例让大模型预测初创企业成败，无需训练。
Predicting Startup Success Using Large Language Models: A Novel In-Context Learning Approach
- 基于相似度选历史案例作上下文示例，不需训练模型。
- 仅用50个标注案例就达到高准确率，优于传统方法。
- 适合数据少的风投机构做早期项目筛选决策。
早期风投项目若成功可带来高额回报，但因数据稀缺（如多数风投公司仅掌握几十家初创企业的信息及成败情况），传统机器学习方法受限于对大规模标注数据的需求，难以有效应用。为此，我们提出一种无需模型训练的上下文学习框架kNN-ICL，利用大语言模型仅通过少量已知成败的初创企业作为示例进行预测。该方法基于k近邻思想，选取与待预测项目最相似的历史案例作为上下文示例。基于Crunchbase真实数据集的实验表明，kNN-ICL在预测准确率上优于监督学习基线和原始上下文学习方法。进一步分析显示，仅需50个上下文示例即可实现较高的平衡准确率。结果证明，上下文学习可成为数据稀缺环境下风投机构的重要决策工具。
原文摘要 · Abstract (English)
Venture capital (VC) investments in early-stage startups that end up being successful can yield high returns. However, predicting early-stage startup success remains challenging due to data scarcity (e.g., many VC firms have information about only a few dozen of early-stage startups and whether they were successful). This limits the effectiveness of traditional machine learning methods that rely on large labeled datasets for model training. To address this challenge, we propose an in-context learning framework for startup success prediction using large language models (LLMs) that requires no model training and leverages only a small set of labeled startups as demonstration examples. Specifically, we propose a novel k-nearest-neighbor-based in-context learning framework, called kNN-ICL, which selects the most relevant past startups as examples based on similarity. Using real-world profiles from Crunchbase, we find that the kNN-ICL approach achieves higher prediction accuracy than supervised machine learning baselines and vanilla in-context learning. Further, we study how performance varies with the number of in-context examples and find that a high balanced accuracy can be achieved with as few as 50 examples. Together, we demonstrate that in-context learning can serve as a decision-making tool for VC firms operating in data-scarce environments.
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。