Papers by Răzvan-Alexandru Smădu

4 papers
GRAF: Graph Retrieval Augmented by Facts for Romanian Legal Multi-Choice Question Answering (2025.findings-acl)

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Challenge: Question answering systems have been used for various domains and languages.
Approach: They propose a novel approach for question answering (QA) that combines a dataset of Romanian legal questions with a CROL corpus of laws.
Outcome: The proposed approach achieves competitive results with generally accepted state-of-the-art methods and even exceeds them in most settings.
Investigating Large Language Models for Complex Word Identification in Multilingual and Multidomain Setups (2024.emnlp-main)

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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.
Domain Adaptation in Multilingual and Multi-Domain Monolingual Settings for Complex Word Identification (2022.acl-long)

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Challenge: Existing datasets for complex word identification (CWI) are limited and the difficulty of the task is augmented by the scarcity of input examples.
Approach: They propose a novel training technique for the complex word identification task based on domain adaptation to improve character and context representations.
Outcome: The proposed training technique improves the target character and context representations and also smooths differences between datasets.
RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams (2026.findings-eacl)

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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.

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