Pre-trained Transformers are challenging human performances in many NLP tasks. The
massive datasets used for pre-training seem to be the key to their success on existing tasks …

Few-shot learning is a deep learning subfield that is the focus of research nowadays. This
paper addresses the research question of whether a triplet-trained Siamese network, initially …

Understanding textual description to generate code seems to be an achieved capability of
instruction-following Large Language Models (LLMs) in zero-shot scenario. However, there …

While recent research increasingly showcases the remarkable capabilities of Large
Language Models (LLMs), it's vital to confront their hidden pitfalls. Among these challenges …

Large language models (LLMs) require a significant redesign in solutions to preserve
privacy in data-intensive applications due to their text-generation capabilities. Indeed, LLMs …

Relational databases play an important role in business, science, and beyond. However, the
operability of relational databases is restricted to users familiar with specific languages such …

Large language models (LLMs) have recently revolutionized automated text understanding
and generation. The performance of these models relies on the high number of parameters …

Ontology Alignment (OA) is a complex, demanding and error-prone task, requiring the
intervention of domain and Semantic Web experts. Automating the alignment process thus …

Curriculum Learning (CL) is emerging as a relevant technique to reduce the cost of pre-
training Large Language Models. The idea, tested for the English language, is to train LLMs …

Abstract Instruction-tuned Large Language Models (It-LLMs) are changing NLP thanks to
their easy accessibility. These models seem able to grasp language, solve complex tasks …