An LLM-feature-driven ensemble predicts billion-dollar startup outcomes with 9.8X to 11.1X the precision of a random classifier, but the label and the model's intermediate target are both funding, so the result partly restates what the label already encodes.
Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques
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abstract
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.
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From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
An LLM-feature-driven ensemble predicts billion-dollar startup outcomes with 9.8X to 11.1X the precision of a random classifier, but the label and the model's intermediate target are both funding, so the result partly restates what the label already encodes.