Exploring Text-Embedding Retrieval Models for the Italian Language
Authors: Yuri Noviello, Fabio Tamburini (Università di Bologna, 2024)
Topic: In-domain IR and IE

Abstract
Text retrieval systems have become essential in the field of natural language processing (NLP), serving as the backbone for applications such as search engines, document indexing, and information retrieval. With the rise of generative AI, particularly Retrieval-Augmented Generation (RAG) systems, the demand for robust text retrieval models has increased. However, existing large language models (LLMs) and datasets are often insufficiently optimized for Italian, limiting their performance in Italian text retrieval tasks. This paper addresses this gap by proposing both a data collection and specialized models tailored for Italian text retrieval. Through extensive experimentation, we analyze the improvements and limitations in retrieval performance, paving the way for more effective Italian NLP applications.

Introduction
In recent years, text retrieval systems have emerged as a cornerstone of the natural language processing (NLP) field. These systems are crucial in various applications, including search engines, document indexing, and information retrieval tasks. Their primary function is to fetch relevant pieces of text from large corpora, enabling efficient and accurate information access. This capability is crucial for numerous industries, including legal, medical, and customer service sectors, where timely and precise information retrieval can significantly impact decision-making processes. With the advent of generative AI, the importance of text retrieval systems has only amplified. Advanced systems, particularly chatbots based on Retrieval-Augmented Generation (RAG) AUTHOR, have become essential tools for various purposes. RAG systems combine retrieval mechanisms with generative models to produce contextually relevant and accurate responses in conversational AI applications. This integration has enhanced the capabilities of chatbots, making them more efficient in providing precise information and engaging in meaningful dialogues. Despite the impressive performance of recent large language models (LLMs) as conversational agents in Italian contexts, there remains a notable gap in the resources and models specifically designed for Italian text retrieval tasks. This shortfall highlights a significant area for improvement and development within the Italian NLP community. To address this gap, our work aims to propose both novel datasets and specialized models optimized for Italian text retrieval. By focusing exclusively on the Italian language, we strive to enhance the performance of retrieval tasks. The primary contribution of this paper is the introduction of a comprehensive Italian text retrieval system, encompassing both a curated dataset collection and specialized language models. Through extensive experimentation and rigorous evaluation, we demonstrate the effectiveness of our approach, setting the stage for more advanced and reliable Italian text retrieval solutions applicable across diverse tasks.

Conclusion
This work presents a comprehensive study on models and datasets focused on Information Retrieval (IR) for Italian documents. The primary contribution of this paper lies in illustrating a strategy for fine-tuning Large Language Models (LLMs) to achieve effective semantic representations of Italian texts. Additionally, we provide original models and datasets that serve as a starting point to bridge the performance gap between models designed for Italian and those optimized for other languages. Our results demonstrate that the proposed models achieve performance comparable with state-of-the-art models for medium-sized documents and even surpass them when dealing with datasets containing very long documents. This suggests that our tailored approach to Italian text retrieval is not only viable but also highly effective.