Papers by Florin Pop
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups (2024.emnlp-main)
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Răzvan-Alexandru Smădu, David-Gabriel Ion, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel
| Challenge: | Large language models (LLMs) are popular in the Natural Language Processing community because of their versatility and capability to solve unseen tasks in zero/few-shot settings. |
| Approach: | They investigate the use of large language models in CWI, LCP, and MWE settings by evaluating their use in zero-shot, few-shot and fine-tuning settings. |
| Outcome: | The proposed models struggle in certain conditions or achieve comparable results against existing methods. |
RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams (2026.findings-eacl)
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Andrei Vlad Man, Răzvan-Alexandru Smădu, Cristian-George Craciun, Dumitru-Clementin Cercel, Florin Pop, Mihaela-Claudia Cercel
| Challenge: | a growing need for tools that support legal education, especially in under-resourced languages such as Romanian . we evaluate the capabilities of large language models and vision-language models in legal education . |
| Approach: | They evaluate the capabilities of Large Language Models and Vision-Language Models in Romanian driving law through textual and visual question-answering tasks. |
| Outcome: | The proposed model improves retrieval performance and QA accuracy in Romanian driving tests. |
RoLargeSum: A Large Dialect-Aware Romanian News Dataset for Summary, Headline, and Keyword Generation (2025.coling-main)
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Andrei-Marius Avram, Mircea Timpuriu, Andreea Iuga, Vlad-Cristian Matei, Iulian-Marius Taiatu, Tudor Găină, Dumitru-Clementin Cercel, Mihaela-Claudia Cercel, Florin Pop
| Challenge: | Using supervised automatic summarization requires sufficient corpora that include pairs of documents and their summaries. |
| Approach: | They propose a large-scale summarization dataset for the Romanian language that is crawled from publicly available news websites. |
| Outcome: | The proposed system performs well in abstractive summarization, which involves generating new sentences that capture the essence of the original text rather than extracting and rephrasing existing sentences. |