7 citations · 15 across the 11 of their papers we have counts for
11 papers
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…
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…
Leveraging Large Language Models for Medical Information Extraction and Query Generation
Georgios Peikos, Pranav Kasela, Gabriella Pasi
This paper introduces a system that integrates large language models (LLMs) into the clinical trial retrieval process, enhancing the effectiveness of matching patients with eligibl…
Synthetic Data Generation with Large Language Models for Personalized Community Question Answering
Marco Braga, Pranav Kasela, Alessandro Raganato +1
Personalization in Information Retrieval (IR) is a topic studied by the research community since a long time. However, there is still a lack of datasets to conduct large-scale eval…
DESIRE-ME: Domain-Enhanced Supervised Information REtrieval using Mixture-of-Experts
Pranav Kasela, Gabriella Pasi, Raffaele Perego +1
Open-domain question answering requires retrieval systems able to cope with the diverse and varied nature of questions, providing accurate answers across a broad spectrum of query…
SE-PEF: a Resource for Personalized Expert Finding
Pranav Kasela, Gabriella Pasi, Raffaele Perego
The problem of personalization in Information Retrieval has been under study for a long time. A well-known issue related to this task is the lack of publicly available datasets tha…