5 papers
ScaleCall -- Agentic Tool Calling at Scale for Fintech: Challenges, Methods, and Deployment Insights
Richard Osuagwu, Thomas Cook, Maraim Masoud +2
While Large Language Models (LLMs) excel at tool calling, deploying these capabilities in regulated enterprise environments such as fintech presents unique challenges due to on-pre…
Retrieval Augmented Generation (RAG) for Fintech: Agentic Design and Evaluation
Thomas Cook, Richard Osuagwu, Liman Tsatiashvili +4
Retrieval-Augmented Generation (RAG) systems often face limitations in specialized domains such as fintech, where domain-specific ontologies, dense terminology, and acronyms compli…
Parameter Efficient Fine-Tuning for Deep Learning-Based Full-Waveform Inversion
Koustav Ghosal, Abhranta Panigrahi, Arnav Chavan +2
Seismic full waveform inversion (FWI) has seen promising advancements through deep learning. Existing approaches typically focus on task-specific models trained and evaluated in is…
Enhancing Deep Learning based RMT Data Inversion using Gaussian Random Field
Koustav Ghosal, Arun Singh, Samir Malakar +2
Deep learning (DL) methods have emerged as a powerful tool for the inversion of geophysical data. When applied to field data, these models often struggle without additional fine-tu…
Harnessing Business and Media Insights with Large Language Models
Yujia Bao, Ankit Parag Shah, Neeru Narang +30
This paper introduces Fortune Analytics Language Model (FALM). FALM empowers users with direct access to comprehensive business analysis, including market trends, company performan…