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DataTales: A Benchmark for Real-World Intelligent Data Narration

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Yajing Yang, Qian Liu, Min-Yen Kan

Artificial Intelligence · Submitted 23 Oct 2024 · Published version

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim 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.

how it is stated · asserted 0.55 read the record →

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arXiv ID2410.17859 (version 2)
Pith IDRHPFJYBC, Pith's permanent record of this paper and its review
CitationMin-Yen Kan, Qian Liu, Yajing Yang. "DataTales: A Benchmark for Real-World Intelligent Data Narration." arXiv:2410.17859 (2024). https://pith.science/paper/RHPFJYBC
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The compiled record

The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 3,607 input tokens.

What the abstract itself states, as probabilities: claims first 0.20 · states limitation 0.37 · human participants 0.08 · controlled comparison 0.06 · randomized assignment 0.01 · states quantitative result 0.81 · compares with prior results 0.18 · mentions code or data availability 0.06.

The abstract, sentence by sentence

arXiv listing abstract v2. Results and the main claim are underlined; .

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.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 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. contribution 1.00 0.90 0.05 asserted 1.00
s1 Existing benchmarks often fall short in capturing the requisite analytical complexity for practical applications. gap 0.93 0.04 0.03 asserted 0.96
s2 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. contribution 0.62 0.51 0.05 asserted 0.99
s3 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. interpretation 0.52 0.93 0.84 asserted 0.55

What kind of paper the classifier reports this is: dataset or benchmark 1.00 · evidence from observation or experiment 0.78.

What was examined, and what was not

sources read
  • arXiv listing abstract v2
not examined
  • full text
  • figures and tables
  • references and citing sentences
  • relations to other papers
levels run
  • P abstract
not run
  • S sections
  • T body sentences
  • C citations
  • R cross-paper relations

Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original 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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What this paper does with the arXiv papers it cites

Each citing sentence is taken from the citing paper's LaTeX source and typed by Pith's classifier. A label appears at probability 0.8 or more (0.95 for support), a contest at 0.7 or more; the quoted words are the citing authors'. How each label was checked · JSON

What it cites · 7 sentences without a confident label

Uses the method or tool of 1 paper
Cites as background 3 papers

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