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Founder-GPT: Self-play to evaluate the Founder-Idea fit
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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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Cited by 2 Pith papers
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Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data
An LLM-generated set of yes/no founder questions, combined by simple threshold voting, is reported to predict startup success with 13.1% precision versus a 1.9% base rate, though the supporting evidence has significant gaps.
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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...
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