EMNLP 2024main0 citations

DataTales: A Benchmark for Real-World Intelligent Data Narration

Yajing Yang, Qian Liu, Min-Yen Kan

Abstract

We introduce DataTales, a novel benchmark designed to assess the proficiency of language models in data narration, a task crucial for transforming complex tabular data into accessible narratives. Existing benchmarks often fall short in capturing the requisite analytical complexity for practical applications. DataTales addresses this gap by offering 4.9k financial reports paired with corresponding market data, showcasing the demand for models to create clear narratives and analyze large datasets while understanding specialized terminology in the field. Our findings highlights the significant challenge that language models face in achieving the necessary precision and analytical depth for proficient data narration, suggesting promising avenues for future model development and evaluation methodologies.

BibTeX
@inproceedings{yang-etal-2024-datatales,
    title = "{D}ata{T}ales: A Benchmark for Real-World Intelligent Data Narration",
    author = "Yang, Yajing  and
      Liu, Qian  and
      Kan, Min-Yen",
    editor = "Al-Onaizan, Yaser  and
      Bansal, Mohit  and
      Chen, Yun-Nung",
    booktitle = "Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing",
    month = nov,
    year = "2024",
    address = "Miami, Florida, USA",
    publisher = "Association for Computational Linguistics",
    url = "https://aclanthology.org/2024.emnlp-main.601/",
    doi = "10.18653/v1/2024.emnlp-main.601",
    pages = "10764--10788"
}
DataTales: A Benchmark for Real-World Intelligent Data Narration · EMNLP 2024