1 citations · 1 across the 3 of their papers we have counts for
5 papers
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…
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…
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.…
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…