Hugging Face's logo

Datasets:
unstpb-nlp
/
RoD-TAL

Tasks:
Text Retrieval
Question Answering
Modalities:
Image
Text
Formats:
parquet
Languages:
Romanian
Size:
1K - 10K
ArXiv:
Tags:
legal
Libraries:
Datasets
pandas
Polars
License:
Dataset card Data Studio Files
xet
 Community
1

You need to agree to share your contact information to access this dataset

This repository is publicly accessible, but you have to accept the conditions to access its files and content.

By requesting access you confirm that you will use this dataset exclusively for academic research purposes, will not use it for commercial products and will not redistribute the dataset

Log in or Sign Up to review the conditions and access this dataset content.

RoD-TAL

RoD-TAL is a multimodal benchmark for Romanian driving-license exam tasks, introduced in:

RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams
Findings of EACL 2026, arXiv: 2507.19666

The dataset supports four task families:

  • IR (Information Retrieval): retrieve relevant legal articles
  • QA (Question Answering): answer text-only driving-exam questions
  • VIR (Visual Information Retrieval): retrieve legal references and indicators from visual questions
  • VQA (Visual Question Answering): answer image-grounded driving-exam questions

What is included

This dataset repository contains:

  • Question splits (split_1 to split_4): exam-style queries with metadata and answer options
  • Legal corpus (corpus_laws): article-level Romanian traffic-law passages
  • Traffic-sign corpus (corpus_traffic_signs): sign metadata and sign images
  • Qrels for legal retrieval (qrels_laws): query-document relevance labels
  • Qrels for sign retrieval (qrels_traffic_signs): query-sign relevance labels
  • Images (images/questions, images/traffic_signs): visual assets referenced by multimodal splits

Split overview

  • split_1: text QA/IR split with train and test subsets
  • split_2: additional text QA/IR split
  • split_3: multimodal split (includes traffic_signs and image field)
  • split_4: multimodal split (includes traffic_signs and image field)

For qrels_laws, the current release includes non-empty splits: qrels_queries_split_1, qrels_queries_split_1_train, qrels_queries_split_1_test, and qrels_queries_split_3.

Usage 

from datasets import load_dataset

# Text-only splits (train/test available for split_1)
queries_train = load_dataset("unstpb-nlp/RoD-TAL", "split_1", split="split_1_train")
queries_test = load_dataset("unstpb-nlp/RoD-TAL", "split_1", split="split_1_test")

# Additional text split
queries_split_2 = load_dataset("unstpb-nlp/RoD-TAL", "split_2", split="split_2_all")

# Legal corpus
corpus_laws = load_dataset("unstpb-nlp/RoD-TAL", "corpus_laws", split="corpus")

# Retrieval labels for legal IR
qrels_laws_train = load_dataset("unstpb-nlp/RoD-TAL", "qrels_laws", split="qrels_queries_split_1_train")
qrels_laws_test = load_dataset("unstpb-nlp/RoD-TAL", "qrels_laws", split="qrels_queries_split_1_test")

# Visual/multimodal splits
queries_visual_split_3 = load_dataset("unstpb-nlp/RoD-TAL", "split_3", split="split_3_all")
queries_visual_split_4 = load_dataset("unstpb-nlp/RoD-TAL", "split_4", split="split_4_all")

# Traffic sign retrieval labels
qrels_signs = load_dataset("unstpb-nlp/RoD-TAL", "qrels_traffic_signs", split="qrels_queries_split_3")

Related resources

The experiment notebooks in the GitHub repository cover corpus scraping, IR, QA, VIR, VQA, and post-hoc analyses.

Citation

If you use RoD-TAL in your research, please cite our paper:

@misc{man2025rodtalbenchmarkansweringquestions,
      title={RoD-TAL: A Benchmark for Answering Questions in Romanian Driving License Exams},
      author={Andrei Vlad Man and Răzvan-Alexandru Smădu and Cristian-George Craciun and Dumitru-Clementin Cercel and Florin Pop and Mihaela-Claudia Cercel},
      year={2025},
      eprint={2507.19666},
      archivePrefix={arXiv},
      primaryClass={cs.CL},
      url={https://arxiv.org/abs/2507.19666}
}
Downloads last month
30

Models trained or fine-tuned on unstpb-nlp/RoD-TAL

Collection including unstpb-nlp/RoD-TAL

Paper for unstpb-nlp/RoD-TAL