# Do LLMs Understand Romanian Driving Laws? A Study on Multimodal and Fine-Tuned Question Answering

Source: [Do LLMs Understand Romanian Driving Laws? A Study on Multimodal and Fine-Tuned Question Answering](<https://www.sota2.com/research/paper/68fa7bb41e209131cea8a03a>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-26T05:46:25.721Z

Authors: Eduard Barbu, Adrian Marius Dumitran

Published: 2025-09-28T07:58:00.000Z

## Abstract

Ensuring that both new and experienced drivers master current traffic rules is critical to road safety. This paper evaluates Large Language Models (LLMs) on Romanian driving-law QA with explanation generation. We release a 1{,}208-question dataset (387 multimodal) and compare text-only and multimodal SOTA systems, then measure the impact of domain-specific fine-tuning for Llama 3.1-8B-Instruct and RoLlama 3.1-8B-Instruct. SOTA models perform well, but fine-tuned 8B models are competitive. Textual descriptions of images outperform direct visual input. Finally, an LLM-as-a-Judge assesses explanation quality, revealing self-preference bias. The study informs explainable QA for less-resourced languages.

## Recorded benchmark results

A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage.

## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2509.23715>)

[PDF](<https://arxiv.org/pdf/2509.23715.pdf>)