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.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
cs.LG 1years
2025 1verdicts
CONDITIONAL 1representative citing papers
citing papers explorer
-
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.