Papers by Andrei Stefan Bejgu
Concept-pedia: a Wide-coverage Semantically-annotated Multimodal Dataset (2025.emnlp-main)
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| Challenge: | Current evaluations for Vision-language Models remain heavily anchored to ImageNet . |
| Approach: | They propose a large-scale semantically-annotated multimodal resource that extends the range of visual concepts, including diverse abstract categories. |
| Outcome: | The proposed model expands the range of visual concepts, including diverse abstract categories. |
Optimizing LLMs for Italian: Reducing Token Fertility and Enhancing Efficiency Through Vocabulary Adaptation (2025.findings-naacl)
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Luca Moroni, Giovanni Puccetti, Pere-Lluís Huguet Cabot, Andrei Stefan Bejgu, Alessio Miaschi, Edoardo Barba, Felice Dell’Orletta, Andrea Esuli, Roberto Navigli
| Challenge: | Pretrained Large Language Models (LLMs) are mainly designed for the English language, but are not optimized for non-English languages due to language contamination or multilingual pretraining data. |
| Approach: | They propose a method that leverages neural mapping for vocabulary substitution to optimize LLMs for the Italian language. |
| Outcome: | The proposed method reduces token fertility by 25% and improves grounded alignment strategies. |