Automating Venture Capital: Founder assessment using LLM-powered segmentation, feature engineering and automated labeling techniques

Autor:	Ozince, Ekin, Ihlamur, Yiğit
Rok vydání:	2024
Předmět:	Computer Science - Computation and Language Computer Science - Artificial Intelligence
Druh dokumentu:	Working Paper
Popis:	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. Comment: For the relevant code, see https://github.com/velapartners/moneyball-LLM-based-founder-features.git
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2407.04885 Zobrazit plný text záznamu View this record from Arxiv