Evaluating startups is inherently challenging in entrepreneurial finance, where investors confront severe information asymmetry and limited quantitative data. Leveraging a novel expert network call data, we develop an LLM-Bayesian model that analyzes these conversations at the question-answer turn level, extracting semantic and evaluative signals via large language models (LLMs) and aggregating them in a sequential Bayesian architecture. The model dynamically updates beliefs as additional expert calls occur and attenuates contradictory assessments, which are absent from existing text-based screening tools. Empirically, our model outperforms state-of-the-art benchmarks by 6.691% in F1-score and increases portfolio-level Return on Investment by 15.255%. Attention and ablation analyses reveal that conversational cues are particularly informative for technologically complex startups, young firms, diverse founding teams, and firms with low public visibility. By converting expert dialogue into continually updated probabilities, our model advances research in entrepreneurial finance and information systems and offers policy implications for improving funding outcomes for informationally disadvantaged startups.

翻译：在创业金融领域，初创企业评估本质上面临挑战，投资者需应对严重的信息不对称和有限的定量数据。利用新颖的专家网络访谈数据，我们开发了一种LLM-贝叶斯模型，该模型在问答轮次层面分析这些对话，通过大语言模型（LLMs）提取语义和评估信号，并在序列化贝叶斯架构中进行聚合。该模型随着新增专家访谈动态更新信念，并弱化矛盾评估——这些功能是现有基于文本的筛选工具所缺失的。实证结果表明，我们的模型在F1分数上优于最先进基准6.691%，并将投资组合层面的投资回报率提升15.255%。注意力机制与消融分析表明，对话线索对技术复杂度高的初创企业、年轻公司、多元化创始团队及公众关注度低的企业具有特别显著的信息价值。通过将专家对话转化为持续更新的概率估计，我们的模型推动了创业金融与信息系统领域的研究，并为改善信息弱势初创企业的融资结果提供了政策启示。