用可解释的AI模型，仅靠少量数据预测初创企业成败
Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning
- 在提示中嵌入自然语言策略，让大模型按明确逻辑推理
- 仅需少量标注数据，预测准确率是随机猜测的20倍以上
- 适合风险投资机构和专家协同优化决策逻辑
早期创业投资具有高风险、数据稀缺、结果不确定的特点。传统机器学习方法需要大量有标签数据并进行复杂微调，且模型黑箱难懂，难以被领域专家理解和改进。本文提出一种基于记忆增强型大语言模型（LLM）的透明、高效投资决策框架，采用上下文学习（ICL）技术。核心在于将自然语言形式的决策策略直接嵌入模型提示，使模型具备可解释的推理模式，便于人类专家理解、审计和迭代优化。我们设计了一种轻量级训练流程，结合少样本学习与上下文学习循环，使模型能根据结构化反馈逐步更新决策策略。系统仅需极少监督信号，无需梯度优化，在不依赖大规模数据的情况下，预测准确率远超现有基准：比随机猜测（成功率1.9%）高出20倍以上，也比顶级风投机构平均5.6%的成功率高出7.1倍。
原文摘要 · Abstract (English)
Early-stage startup investment is a high-risk endeavor characterized by scarce data and uncertain outcomes. Traditional machine learning approaches often require large, labeled datasets and extensive fine-tuning, yet remain opaque and difficult for domain experts to interpret or improve. In this paper, we propose a transparent and data-efficient investment decision framework powered by memory-augmented large language models (LLMs) using in-context learning (ICL). Central to our method is a natural language policy embedded directly into the LLM prompt, enabling the model to apply explicit reasoning patterns and allowing human experts to easily interpret, audit, and iteratively refine the logic. We introduce a lightweight training process that combines few-shot learning with an in-context learning loop, enabling the LLM to update its decision policy iteratively based on structured feedback. With only minimal supervision and no gradient-based optimization, our system predicts startup success far more accurately than existing benchmarks. It is over 20x more precise than random chance, which succeeds 1.9% of the time. It is also 7.1x more precise than the typical 5.6% success rate of top-tier venture capital (VC) firms.
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。