---
title: "RoD-TAL — paper by Man et al. (2026) — summary and ideas"
description: "The authors build RoD-TAL, which has two parts. 86.4% accuracy. 42 references. Among them: 0."
authors: ["Andrei Vlad Man", "Răzvan-Alexandru Smădu", "Cristian-George Craciun", "Dumitru-Clementin Cercel", "Florin Pop", "Mihaela-Claudia Cercel"]
year: 2026
venue: "Findings of the Association for Computational Linguistics: EACL 2026"
source: "https://aclanthology.org/2026.findings-eacl.295/"
doi: "10.18653/v1/2026.findings-eacl.295"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/man-2026-rod-tal-benchmark-answering-questions/"
---
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# RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams

**Authors:** Andrei Vlad Man, Răzvan-Alexandru Smădu, [Cristian-George Craciun](https://md.research.elamur.ai/free/en/authors/cristian-george-craciun/), Dumitru-Clementin Cercel, Florin Pop and Mihaela-Claudia Cercel · **Year:** 2026 · **Venue:** [Findings of the Association for Computational Linguistics: EACL 2026](https://md.research.elamur.ai/free/en/venues/findings/) ([Findings of the Association for Computational Linguistics 2026](https://md.research.elamur.ai/free/en/venues/findings/2026/)) · **Publisher:** [Association for Computational Linguistics](https://md.research.elamur.ai/free/en/publishers/association-for-computational-linguistics/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2026.findings-eacl.295/) · [PDF](https://aclanthology.org/2026.findings-eacl.295.pdf) · [DOI 10.18653/v1/2026.findings-eacl.295](https://doi.org/10.18653/v1/2026.findings-eacl.295) · [OpenAlex](https://openalex.org/W7140134105)

**Citations:** 0 citations (OpenAlex, 2026-10-14)

## Summary
The paper tackles a gap in legal NLP for low-resource languages. It asks how well LLMs and VLMs handle Romanian traffic law when answering driving-license exam questions, both text-only and with images. The authors build RoD-TAL, which has two parts. RoD-Law is a corpus of current Romanian traffic-related legislation plus an annotated catalogue of traffic signs. RoD-QA is a set of multiple-choice exam questions, each linked by human experts to legal references and explanations. Their evaluation spans four tasks: finding relevant documents (IR), answering the exam questions (QA), and the image-based versions of each, namely VIR and VQA. Their RAG pipelines pair a fine-tuned dense retriever (returning the top 10 laws or signs) with o4-mini, chain-of-thought prompting and, for images, generated captions. Fine-tuning the retriever on the domain clearly improves retrieval. Reasoning models with CoT reach accuracy above the official passing threshold. The work offers a reusable Romanian legal benchmark and shows both the promise and the hallucination risks of using LLMs in legal education.

## Key points
- RoD-TAL contains about 1.2K questions from the Școala Rutieră platform, 400 of them with images. RoD-Law covers six sources, including 225 Traffic Regulation Rules articles, 147 Road Code articles and 140 traffic signs in 11 categories, current as of March 2025.
- The best RAG setups with o4-mini reach 86.4% accuracy on text QA and 78.3% on VQA for Romanian.
- In VQA, giving the model the raw image beats giving it a caption by 7–8%. Removing RAG costs at least 5%.
- The error analysis finds two failure modes. Models over-select answers, which hurts exact match, and they hallucinate citations more often when citing more documents. Mistral 3.1 often breaks the required output format.
- Code and resources are released on GitHub, with the scraping and cleaning code under the MIT license.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

## Institutions

- [Technical University of Munich](https://md.research.elamur.ai/free/en/universities/technical-university-of-munich/) — University, Munich, Germany · 55 works · 48,474 citations
- [University of Bucharest](https://md.research.elamur.ai/free/en/universities/university-of-bucharest/) — University, Bucharest, Romania · 21 works · 516 citations
- [Universitatea Națională de Știință și Tehnologie Politehnica București](https://md.research.elamur.ai/free/en/universities/universitatea-nationala-de-stiinta-si-tehnologie-politehnica-bucuresti/) — University, Bucharest, Romania · 5 works · 265 citations
- [Université Paris 1 Panthéon-Sorbonne](https://md.research.elamur.ai/free/en/universities/universite-paris-1-pantheon-sorbonne/) — University, Paris, France · 2 works · 45 citations

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🧭 Topics (3): [📏 Evaluation & benchmarks](https://md.research.elamur.ai/free/en/topics/evaluation-benchmarks/) · 1,220 works · [❓ Question answering](https://md.research.elamur.ai/free/en/topics/question-answering/) · 441 works · [⚖️ Legal AI](https://md.research.elamur.ai/free/en/topics/legal-ai/) · 134 works

## Cite

Cite as: Andrei Vlad Man, Răzvan-Alexandru Smădu, Cristian-George Craciun, Dumitru-Clementin Cercel, Florin Pop and Mihaela-Claudia Cercel. 2026. RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams. Findings of the Association for Computational Linguistics: EACL 2026.

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{man2026rod,
  title = {{RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams}},
  author = {Andrei Vlad Man, Răzvan-Alexandru Smădu, Cristian-George Craciun, Dumitru-Clementin Cercel, Florin Pop and Mihaela-Claudia Cercel},
  booktitle = {Findings of the Association for Computational Linguistics: EACL 2026},
  year = {2026},
  doi = {10.18653/v1/2026.findings-eacl.295},
  url = {https://doi.org/10.18653/v1/2026.findings-eacl.295}
}
```

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42. Distribution of Number of Legal Articles 264 249 250 Dataset Statistics In this section, we present statistics of the RoD-TAL dataset, which comprises two main components: the legal corpus RoD-Law and the multiple-choice question-answering dataset RoD-QA. We analyze the characteristics and distribut… [find](https://scholar.google.com/scholar?q=Distribution+of+Number+of+Legal+Articles+264+249+250+Dataset+Statistics+In+this+section%2C+we+present+statistics+of+the+RoD%2DTAL+dataset%2C+which+comprises+two+main+components%3A+the+legal+corpus+RoD%2DLaw+and)

*Updated 1 November 2026*