27 citations · 95 across the 53 of their papers we have counts for
15 papers · 1 filter
Explainable Federated Learning for U.S. State-Level Financial Distress Modeling
Lorenzo Carta, Fernando Spadea, Oshani Seneviratne
We present the first application of federated learning (FL) to the U.S. National Financial Capability Study, introducing an interpretable framework for predicting consumer financia…
Resource-Efficient LLM Application for Structured Transformation of Unstructured Financial Contracts
Maruf Ahmed Mridul, Oshani Seneviratne
The transformation of unstructured legal contracts into standardized, machine-readable formats is essential for automating financial workflows. The Common Domain Model (CDM) provid…
Aligning Language Models with Investor and Market Behavior for Financial Recommendations
Fernando Spadea, Oshani Seneviratne
Most financial recommendation systems often fail to account for key behavioral and regulatory factors, leading to advice that is misaligned with user preferences, difficult to inte…
Parallel and Multi-Stage Knowledge Graph Retrieval for Behaviorally Aligned Financial Asset Recommendations
Fernando Spadea, Oshani Seneviratne
Large language models (LLMs) show promise for personalized financial recommendations but are hampered by context limits, hallucinations, and a lack of behavioral grounding. Our pri…
Curriculum-Guided Reinforcement Learning for Synthesizing Gas-Efficient Financial Derivatives Contracts
Maruf Ahmed Mridul, Oshani Seneviratne
Smart contract-based automation of financial derivatives offers substantial efficiency gains, but its real-world adoption is constrained by the complexity of translating financial…
MetaExplainer: A Framework to Generate Multi-Type User-Centered Explanations for AI Systems
Shruthi Chari, Oshani Seneviratne, Prithwish Chakraborty +2
Explanations are crucial for building trustworthy AI systems, but a gap often exists between the explanations provided by models and those needed by users. To address this gap, we…
Avoiding Over-Personalization with Rule-Guided Knowledge Graph Adaptation for LLM Recommendations
Fernando Spadea, Oshani Seneviratne
We present a lightweight neuro-symbolic framework to mitigate over-personalization in LLM-based recommender systems by adapting user-side Knowledge Graphs (KGs) at inference time.…
FinSurvival: A Suite of Large Scale Survival Modeling Tasks from Finance
Aaron Green, Zihan Nie, Hanzhen Qin +2
Survival modeling predicts the time until an event occurs and is widely used in risk analysis; for example, it's used in medicine to predict the survival of a patient based on cens…
AI4Contracts: LLM & RAG-Powered Encoding of Financial Derivative Contracts
Maruf Ahmed Mridul, Ian Sloyan, Aparna Gupta +1
Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) are reshaping how AI systems extract and organize information from unstructured text. A key challenge is desig…
Terminators: Terms of Service Parsing and Auditing Agents
Maruf Ahmed Mridul, Inwon Kang, Oshani Seneviratne
Terms of Service (ToS) documents are often lengthy and written in complex legal language, making them difficult for users to read and understand. To address this challenge, we prop…
SemCAFE: When Named Entities make the Difference Assessing Web Source Reliability through Entity-level Analytics
Gautam Kishore Shahi, Oshani Seneviratne, Marc Spaniol
With the shift from traditional to digital media, the online landscape now hosts not only reliable news articles but also a significant amount of unreliable content. Digital media…
Federated Fine-Tuning of Large Language Models: Kahneman-Tversky vs. Direct Preference Optimization
Fernando Spadea, Oshani Seneviratne
We evaluate Kahneman-Tversky Optimization (KTO) as a fine-tuning method for large language models (LLMs) in federated learning (FL) settings, comparing it against Direct Preference…
Blockchain-based Framework for Scalable and Incentivized Federated Learning
Bijun Wu, Oshani Seneviratne
Federated Learning (FL) enables collaborative model training without sharing raw data, preserving privacy while harnessing distributed datasets. However, traditional FL systems oft…
Explainability-Driven Quality Assessment for Rule-Based Systems
Oshani Seneviratne, Brendan Capuzzo, William Van Woensel
This paper introduces an explanation framework designed to enhance the quality of rules in knowledge-based reasoning systems based on dataset-driven insights. The traditional metho…
On Learning Representations for Tabular Data Distillation
Inwon Kang, Parikshit Ram, Yi Zhou +2
Dataset distillation generates a small set of information-rich instances from a large dataset, resulting in reduced storage requirements, privacy or copyright risks, and computatio…