5 papers
Random Rule Forest (RRF): Interpretable and Manageable Ensembles of LLM-Generated Questions for Predicting Success from Unstructured Data
Ben Griffin, Aaron Ontoyin Yin, Diego Vidaurre +4
Many high-stakes screening tasks require predicting rare outcomes from unstructured text, where errors are costly and decisions must be auditable. We introduce Random Rule Forest (…
VCBench: Benchmarking LLMs in Venture Capital
Rick Chen, Joseph Ternasky, Afriyie Samuel Kwesi +7
Benchmarks such as SWE-bench and ARC-AGI demonstrate how shared datasets accelerate progress toward artificial general intelligence (AGI). We introduce VCBench, the first benchmark…
PHBench: A Benchmark for Predicting Startup Series A Funding from Product Hunt Launch Signals
Yagiz Ihlamur, Ben Griffin, Rick Chen
Structured launch signals on Product Hunt contain statistically significant predictive information for Series A funding outcomes. We construct PHBench from 67,292 featured Product…
CoFEE: Reasoning Control for LLM-Based Feature Discovery
Maximilian Westermann, Ben Griffin, Aaron Ontoyin Yin +6
Feature discovery from complex unstructured data is fundamentally a reasoning problem: it requires identifying abstractions that are predictive of a target outcome while avoiding l…
From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
Anirudh Jaidev Mahesh, Ben Griffin, Fuat Alican +8
Large language models (LLMs) are increasingly used for high-stakes decision-making, yet existing approaches struggle to reconcile scalability, interpretability, and reproducibility…