10 citations · 30 across the 11 of their papers we have counts for
11 papers · 1 filter
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…
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…
Denoising Attention for Query-aware User Modeling in Personalized Search
Elias Bassani, Pranav Kasela, Gabriella Pasi
The personalization of search results has gained increasing attention in the past few years, thanks to the development of Neural Networks-based approaches for Information Retrieval…
Utilizing ChatGPT to Enhance Clinical Trial Enrollment
Georgios Peikos, Symeon Symeonidis, Pranav Kasela +1
Clinical trials are a critical component of evaluating the effectiveness of new medical interventions and driving advancements in medical research. Therefore, timely enrollment of…
SE-PQA: Personalized Community Question Answering
Pranav Kasela, Marco Braga, Gabriella Pasi +1
Personalization in Information Retrieval is a topic studied for a long time. Nevertheless, there is still a lack of high-quality, real-world datasets to conduct large-scale experim…