Title: Language Modeling in Finance: Evaluation, Extraction, and Economic Insight 

Date: March 13, 2026 

Time: 10:00 AM 

Location: Coda C1108 Brookhaven 

Zoom Link: https://gatech.zoom.us/j/97022246037 

Agam Shah 

Machine Learning PhD Candidate 

School of Computational Science and Engineering 
Georgia Institute of Technology 

Committee 

1. Sudheer Chava (Advisor), CoB and CSE, Georgia Tech 

2. Chao Zhang (Co-Advisor), CSE, Georgia Tech 

3. Aditya Prakash, CSE, Georgia Tech 

4. Srinivas Aluru, CSE, Georgia Tech 

5. Indrajit Mitra, Federal Reserve Bank of Atlanta 

Abstract 

Over the past decade, advances in self-attention mechanisms and foundation models have transformed Natural Language Processing (NLP), creating new opportunities and challenges for high-stakes domains such as finance. This work positions finance as both a critical application area and a rigorous diagnostic lens for evaluating the reasoning, bias, and reliability of Large Language Models (LLMs). It uncovers structural limitations of generalist models in financial contexts and introduces calibration and weak-supervision frameworks to address these challenges. At the same time, it develops specialized domain-adapted architectures, datasets, and benchmarks for financial information extraction, institutional discourse analysis, and multimodal economic forecasting. Together, these contributions demonstrate that adapting NLP systems to the financial language enables more reliable AI systems while generating actionable economic insight with impact beyond finance.