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Source note3 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 researchers introduce DataGovBench, a new benchmark for evaluating large language models (LLMs) in practical scenarios of real-world data analysis. The benchmark tests the models' ability to answer complex questions and discover insights from large multi-tabular datasets, tasks that are often overlooked by current evaluation methods. Experiments with state-of-the-art LLMs show significant performance gaps on this new benchmark, indicating the need for further research to create LLMs capable of satisfying real-world data analytics demands.

What the Document Says

  • DataGovBench is a new benchmark derived from governmental open data for evaluating large language models (LLMs) in practical scenarios.
  • The benchmark includes two tasks: Table QA and Table Insight, designed to test the ability of models to solve complex decomposable questions, produce textual answers or visualizations, and 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 on the new benchmark.
  • The 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
  • benchmark
  • Table QA
  • Table Insight
  • state-of-the-art LLMs
  • real-world data analytics

Claims Made by the Document

  • Current benchmarks for evaluating large language models fail to reflect real-world settings.
  • DataGovBench provides a challenging benchmark for advancing research on LLMs capable of answering analytical queries and discovering insights from data.

Evidence Used

  • The text describes the lack of real-world reflection in current benchmarks, the introduction of DataGovBench to fill this gap, and the significant performance gaps found in experiments with state-of-the-art LLMs on this new benchmark.

Methods / Approach

  • Comprehensive experiments were conducted using state-of-the-art large language models. The specific methodology for these experiments is not detailed in the provided text.

Limitations Stated by the Authors

The text does not mention any explicit limitations by the authors.

Limitations Noted by the Agent

Agent interpretation — not from the source.

The limitations of the agent include the lack of details on the methodology used for the experiments, and possible biases or assumptions inherent in the design and implementation of DataGovBench.

Open Questions

  • What specific state-of-the-art large language models were used in the experiments?
  • What was the exact methodology for conducting these experiments?

Notes for Future Q&A

May include interpretation. Labeled where it does.

The provided text does not contain enough information to answer these questions. More details about the experiments and the specific LLMs used would be needed.

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.