12 citations · 50 across the 10 of their papers we have counts for
6 papers · 1 filter
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…
Clue-Instruct: Text-Based Clue Generation for Educational Crossword Puzzles
Andrea Zugarini, Kamyar Zeinalipour, Surya Sai Kadali +3
Crossword puzzles are popular linguistic games often used as tools to engage students in learning. Educational crosswords are characterized by less cryptic and more factual clues t…
Neural paraphrasing by automatically crawled and aligned sentence pairs
Achille Globo, Antonio Trevisi, Andrea Zugarini +3
Paraphrasing is the task of re-writing an input text using other words, without altering the meaning of the original content. Conversational systems can exploit automatic paraphras…
Fast Vocabulary Transfer for Language Model Compression
Leonidas Gee, Andrea Zugarini, Leonardo Rigutini +1
Real-world business applications require a trade-off between language model performance and size. We propose a new method for model compression that relies on vocabulary transfer.…
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…