@inproceedings{man-etal-2026-rod,
    title = "{R}o{D}-{TAL}: A Benchmark for Answering Questions in {R}omanian Driving License Exams",
    author = "Man, Andrei Vlad  and
      Sm{\u{a}}du, R{\u{a}}zvan-Alexandru  and
      Craciun, Cristian-George  and
      Cercel, Dumitru-Clementin  and
      Pop, Florin  and
      Cercel, Mihaela-Claudia",
    editor = "Demberg, Vera  and
      Inui, Kentaro  and
      Marquez, Llu{\'i}s",
    booktitle = "Findings of the {A}ssociation for {C}omputational {L}inguistics: {EACL} 2026",
    month = mar,
    year = "2026",
    address = "Rabat, Morocco",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-eacl/2026.findings-eacl.295/",
    pages = "5562--5602",
    ISBN = "979-8-89176-386-9",
    abstract = "The intersection of AI and legal systems presents a growing need for tools that support legal education, particularly in under-resourced languages such as Romanian. In this work, we aim to evaluate the capabilities of Large Language Models (LLMs) and Vision-Language Models (VLMs) in understanding and reasoning about the Romanian driving law through textual and visual question-answering tasks. To facilitate this, we introduce RoD-TAL, a novel multimodal dataset comprising Romanian driving test questions, text-based and image-based, along with annotated legal references and explanations written by human experts. We implement and assess retrieval-augmented generation (RAG) pipelines, dense retrievers, and reasoning-optimized models across tasks, including Information Retrieval (IR), Question Answering (QA), Visual IR, and Visual QA. Our experiments demonstrate that domain-specific fine-tuning significantly enhances retrieval performance. At the same time, chain-of-thought prompting and specialized reasoning models improve QA accuracy, surpassing the minimum passing grades required for driving exams. We highlight the potential and limitations of applying LLMs and VLMs to legal education. We release the code and resources through the GitHub repository (https://github.com/vladman-25/RoD-TAL)."
}