3 citations · 4 across the 12 of their papers we have counts for
4 papers · 1 filter
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…
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…
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…