most citedFast Vocabulary Transfer for Language Model Compression

10 citations · 20 across the 10 of their papers we have counts for

collaborators

10 papers

cs.CV2024

Caption-Driven Explorations: Aligning Image and Text Embeddings through Human-Inspired Foveated Vision

Dario Zanca, Andrea Zugarini, Simon Dietz +4

Understanding human attention is crucial for vision science and AI. While many models exist for free-viewing, less is known about task-driven image exploration. To address this, we…

cs.CL20241 cited

Dynamic Few-Shot Learning for Knowledge Graph Question Answering

Jacopo D'Abramo, Andrea Zugarini, Paolo Torroni

Large language models present opportunities for innovative Question Answering over Knowledge Graphs (KGQA). However, they are not inherently designed for query generation. To bridg…

cs.CL20242 cited

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…

cs.CL2024

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…

cs.LG2024

Are Compressed Language Models Less Subgroup Robust?

Leonidas Gee, Andrea Zugarini, Novi Quadrianto

To reduce the inference cost of large language models, model compression is increasingly used to create smaller scalable models. However, little is known about their robustness to…

cs.CL20241 cited

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…