1 citations · 1 across the 7 of their papers we have counts for
8 papers
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…
LLM-AR: LLM-powered Automated Reasoning Framework
Rick Chen, Joseph Ternasky, Aaron Ontoyin Yin +3
Large language models (LLMs) can already identify patterns and reason effectively, yet their variable accuracy hampers adoption in high-stakes decision-making applications. In this…
From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
Mihir Kumar, Aaron Ontoyin Yin, Zakari Salifu +4
This paper presents a framework for predicting rare, high-impact outcomes by integrating large language models (LLMs) with a multi-model machine learning (ML) architecture. The app…
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…
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 (…
GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification
Te Pei, Fuat Alican, Aaron Ontoyin Yin +1
This paper introduces GPT-HTree, a framework combining hierarchical clustering, decision trees, and large language models (LLMs) to address this challenge. By leveraging hierarchic…
GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees
Sichao Xiong, Yigit Ihlamur, Fuat Alican +1
Traditional decision tree algorithms are explainable but struggle with non-linear, high-dimensional data, limiting its applicability in complex decision-making. Neural networks exc…