DIALECTIC：一种用于初创企业评估的多智能体系统
DIALECTIC: A Multi-Agent System for Startup Evaluation
AI总结：
DIALECTIC通过多智能体系统提升初创企业评估效率，利用LLM生成事实论证并模拟辩论，生成决策分数以帮助投资者优先排序。
摘要：
风险投资（VC）投资者面临大量的投资机会，但只能投资其中少数，甚至更少最终成功。早期机会的筛选往往受到投资者带宽的限制，需要在评估细致程度和评估机会数量之间做出权衡。为缓解这一权衡，我们引入了DIALECTIC，一种基于LLM的多智能体系统，用于初创企业评估。DIALECTIC首先收集关于初创企业的事实性知识，并将这些事实组织成一个层次化的问题树。然后，它将这些事实综合成支持和反对投资的自然语言论点，并通过模拟辩论迭代地批评和改进这些论点，仅揭示最具有说服力的论点。我们的系统还生成数值决策分数，使投资者能够排序并从而高效地优先考虑机会。我们通过回测对真实的投资机会进行评估，这些机会来自五个VC基金，结果显示DIALECTIC在预测初创企业成功方面的精确度与人类VC相当。
英文摘要：
Venture capital (VC) investors face a large number of investment opportunities but only invest in few of these, with even fewer ending up successful. Early-stage screening of opportunities is often limited by investor bandwidth, demanding tradeoffs between evaluation diligence and number of opportunities assessed. To ease this tradeoff, we introduce DIALECTIC, an LLM-based multi-agent system for startup evaluation. DIALECTIC first gathers factual knowledge about a startup and organizes these facts into a hierarchical question tree. It then synthesizes the facts into natural-language arguments for and against an investment and iteratively critiques and refines these arguments through a simulated debate, which surfaces only the most convincing arguments. Our system also produces numeric decision scores that allow investors to rank and thus efficiently prioritize opportunities. We evaluate DIALECTIC through backtesting on real investment opportunities aggregated from five VC funds, showing that DIALECTIC matches the precision of human VCs in predicting startup success.
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。
