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Source note2 min read

Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities

Source

arXiv

arxiv.org/abs/2607.06482v1

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Plain-Language Brief

The paper introduces DataGovBench, a benchmark for evaluating the performance of Large Language Models (LLMs) in practical scenarios, such as complex data analysis and insight discovery from large multi-tabular datasets. The benchmark includes two tasks: Table QA and Table Insight. Experiments with state-of-the-art LLMs show significant performance gaps across both tasks, suggesting that current LLM-based systems are not yet capable of meeting the demands of real-world data analytics.

What the Document Says

  • The paper presents DataGovBench, a benchmark derived from governmental open data for evaluating LLMs in practical scenarios.
  • DataGovBench consists of two tasks: Table QA and Table Insight.
  • Table QA requires solving complex decomposable questions and producing textual answers or visualizations.
  • Table Insight evaluates the ability of models to generate expert-level findings through exploratory data analysis.
  • Experiments with state-of-the-art LLMs reveal significant performance gaps across both tasks.
  • These results suggest that current LLM-based systems are far from satisfying the demands of real-world data analytics.

Key Concepts Mentioned

  • Large Language Models
  • DataGovBench
  • Table QA
  • Table Insight
  • performance gaps

Claims Made by the Document

  • Current benchmarks for evaluating LLMs in data analysis are not reflective of real-world settings.
  • DataGovBench is a challenging benchmark for advancing research on LLMs capable of answering analytical queries and discovering insights from data.

Evidence Used

  • The paper states that current benchmarks fail to reflect real-world settings.
  • The paper presents DataGovBench as a solution to this problem.

Methods / Approach

UNKNOWN

Limitations Stated by the Authors

UNKNOWN

Limitations Noted by the Agent

Agent interpretation — not from the source.

UNKNOWN

Open Questions

UNKNOWN

Notes for Future Q&A

May include interpretation. Labeled where it does.

UNKNOWN

Raw Abstract / Excerpt

Current benchmarks for evaluating Large Language Models (LLMs) in data analysis often fail to reflect real-world settings. They typically focus on fact retrieval from small tables and overlook the challenges of large multi-tabular datasets, external knowledge integration, and exploratory insight discovery. We introduce DataGovBench, a benchmark derived from governmental open data designed to evaluate LLMs in practical scenarios. The benchmark includes two tasks: Table QA that requires solving complex decomposable questions and producing textual answers or visualizations, and Table Insight that evaluates the ability of models to generate expert-level findings through exploratory data analysis. Comprehensive experiments with state-of-the-art LLMs, both with and without agentic frameworks, reveal significant performance gaps across both tasks. These results suggest that current LLM-based systems remain far from satisfying the demands of real-world data analytics. DataGovBench provides a challenging benchmark for advancing research on LLMs capable of both answering analytical queries and discovering insights from data. Code and sample data are available at https://github.com/SoHasegawa/datagovbench.