3 citations · 7 across the 3 of their papers we have counts for
6 papers
Show Less, Instruct More: Enriching Prompts with Definitions and Guidelines for Zero-Shot NER
Andrew Zamai, Andrea Zugarini, Leonardo Rigutini +2
Recently, several specialized instruction-tuned Large Language Models (LLMs) for Named Entity Recognition (NER) have emerged. Compared to traditional NER approaches, these models h…
Are LLMs Good Cryptic Crossword Solvers?
Abdelrahman Sadallah, Daria Kotova, Ekaterina Kochmar
Cryptic crosswords are puzzles that rely not only on general knowledge but also on the solver's ability to manipulate language on different levels and deal with various types of wo…
BUSTER: a "BUSiness Transaction Entity Recognition" dataset
Andrea Zugarini, Andrew Zamai, Marco Ernandes +1
Albeit Natural Language Processing has seen major breakthroughs in the last few years, transferring such advances into real-world business cases can be challenging. One of the reas…
Multi-word Tokenization for Sequence Compression
Leonidas Gee, Leonardo Rigutini, Marco Ernandes +1
Large Language Models have proven highly successful at modelling a variety of tasks. However, this comes at a steep computational cost that hinders wider industrial uptake. In this…
The WebCrow French Crossword Solver
Giovanni Angelini, Marco Ernandes, Tommaso laquinta +5
Crossword puzzles are one of the most popular word games, played in different languages all across the world, where riddle style can vary significantly from one country to another.…
An energy-based comparative analysis of common approaches to text classification in the Legal domain
Sinan Gultekin, Achille Globo, Andrea Zugarini +2
Most Machine Learning research evaluates the best solutions in terms of performance. However, in the race for the best performing model, many important aspects are often overlooked…