DataTales: A Benchmark for Real-World Intelligent Data Narration

arXiv:2410.17859 · cs.AI · Submitted 2024-10-23 · Read on arXiv

cs.AI

Submitted: 2024-10-23

Updated: 2025-08-23

Comments: Accepted at EMNLP 2024 (main conference, long paper)

Journal ref: Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing, pages 10764-10788, Miami, Florida, USA. Association for Computational Linguistics

DOI: 10.18653/v1/2024.emnlp-main.601

Code: https://github.com/yajingyang/DataTales

License: http://creativecommons.org/licenses/by-nc-sa/4.0/

The gist: 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.

Terminology

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.

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