OUR RESEARCH
A. Sudjianto, A. Zhang, S. Neppalli, T. Joshi, M. Malohlava This paper introduces a comprehensive framework for the evaluation and validation of generative language models (GLMs), with a focus on Retrieval-Augmented Generation (RAG) systems deployed in high-stakes domains such as banking. GLM evaluation is challenging due to open-ended outputs and subjective quality assessments. Leveraging the […]
A. Pangia, A. Sudjianto, A. Zhang, & T. Khan Fair lending practices and model interpretability are crucial concerns in the financial industry, especially given the increasing use of complex machine learning models. In response to the Consumer Financial Protection Bureau’s (CFPB) requirement to protect consumers against unlawful discrimination, we introduce LDA-XGB1, a novel less discriminatory […]
J. Farmer, C. Oian, B. Bowman, & T. Khan The application of deep neural networks towards solving problems in science and engineering has demonstratedencouraging results with the recent formulation of physics-informed neural networks (PINNs). Through thedevelopment of refined machine learning techniques, the high computational cost of obtaining numericalsolutions for partial differential equations governing complicated physical […]
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