4 papers
SeLMRoute: Probabilistic Semantic Evidence for Large Language Model Routing
Vasilis Perifanis, Nikolaos Pavlidis, Symeon Symeonidis
Large language model (LLM) routing aims to select the most suitable model for each incoming query. Most existing routers learn this decision directly from query embeddings, model r…
Federated Learning for Early Prediction of EV Charging Demand
Vasilis Perifanis, Foteini Nikolaidou, Nikolaos Pavlidis +2
Accurate forecasting of electric vehicle (EV) charging demand is critical for grid stability, infrastructure planning, and real-time charging optimization. In this work, we study t…
Large Language Models as Universal Predictors? An Empirical Study on Small Tabular Datasets
Nikolaos Pavlidis, Vasilis Perifanis, Symeon Symeonidis +1
Large Language Models (LLMs), originally developed for natural language processing (NLP), have demonstrated the potential to generalize across modalities and domains. With their in…
Evaluating the Defense Potential of Machine Unlearning against Membership Inference Attacks
Theodoros Tsiolakis, Vasilis Perifanis, Nikolaos Pavlidis +3
Membership Inference Attacks (MIAs) pose a significant privacy risk by enabling adversaries to determine if a specific data point was part of a model's training set. This work empi…