8 papers
DRAMA: Domain Retrieval using Adaptive Module Allocation
Pranav Kasela, Marco Braga, Ophir Frieder +3
Neural models are increasingly used in Web-scale Information Retrieval (IR). However, relying on these models introduces substantial computational and energy requirements, leading…
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…
Investigating Large Language Models' Linguistic Abilities for Text Preprocessing
Marco Braga, Gian Carlo Milanese, Gabriella Pasi
Text preprocessing is a fundamental component of Natural Language Processing, involving techniques such as stopword removal, stemming, and lemmatization to prepare text as input fo…
PARK: Personalized academic retrieval with knowledge-graphs
Pranav Kasela, Gabriella Pasi, Raffaele Perego
Academic Search is a search task aimed to manage and retrieve scientific documents like journal articles and conference papers. Personalization in this context meets individual res…
Investigating Task Arithmetic for Zero-Shot Information Retrieval
Marco Braga, Pranav Kasela, Alessandro Raganato +1
Large Language Models (LLMs) have shown impressive zero-shot performance across a variety of Natural Language Processing tasks, including document re-ranking. However, their effect…
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…