Founder-GPT: Self-play to evaluate the Founder-Idea fit

2023/12/19 by Xiong, Sichao, Ihlamur, Yigit · 2 citations
#Artificial Intelligence (cs.AI) #Computation and Language (cs.CL) #Computational Engineering #FOS: Computer and information sciences #Finance #and Science (cs.CE)

paper · doi:10.48550/arxiv.2312.12037

Abstract

This research introduces an innovative evaluation method for the "founder-idea" fit in early-stage startups, utilizing advanced large language model techniques to assess founders' profiles against their startup ideas to enhance decision-making. Embeddings, self-play, tree-of-thought, and critique-based refinement techniques show early promising results that each idea's success patterns are unique and they should be evaluated based on the context of the founder's background.

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• From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
• Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success

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