1 citations · 1 across the 5 of their papers we have counts for
6 papers
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…
Policy Induction: Predicting Startup Success via Explainable Memory-Augmented In-Context Learning
Xianling Mu, Joseph Ternasky, Fuat Alican +1
Early-stage startup investment is a high-risk endeavor characterized by scarce data and uncertain outcomes. Traditional machine learning approaches often require large, labeled dat…
Reasoning-Based AI for Startup Evaluation (R.A.I.S.E.): A Memory-Augmented, Multi-Step Decision Framework
Jack Preuveneers, Joseph Ternasky, Fuat Alican +1
We present a novel framework that bridges the gap between the interpretability of decision trees and the advanced reasoning capabilities of large language models (LLMs) to predict…
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…