3 papers
cs.IR2026
ExpertLens: Visualizing Embedding Spaces for Post-Hoc Explainability in MoE Enhanced Retrievers
Effrosyni Sokli, Isaac Roberts, Alexander Schulz +2
Neural models, including dense retrievers, have been widely adopted in Information Retrieval (IR), often delivering state-of-the-art performance. Despite their effectiveness, these…
cs.IR2025
Mixture of Experts Approaches in Dense Retrieval Tasks
Effrosyni Sokli, Pranav Kasela, Georgios Peikos +1
Dense Retrieval Models (DRMs) are a prominent development in Information Retrieval (IR). A key challenge with these neural Transformer-based models is that they often struggle to g…
cs.IR2024
Investigating Mixture of Experts in Dense Retrieval
Effrosyni Sokli, Pranav Kasela, Georgios Peikos +1
While Dense Retrieval Models (DRMs) have advanced Information Retrieval (IR), one limitation of these neural models is their narrow generalizability and robustness. To cope with th…