21 citations · 36 across the 13 of their papers we have counts for
13 papers
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…
Reasoning Capabilities and Invariability of Large Language Models
Alessandro Raganato, Rafael Peñaloza, Marco Viviani +1
Large Language Models (LLMs) have shown remarkable capabilities in manipulating natural language across multiple applications, but their ability to handle simple reasoning tasks is…
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…