Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
Score
79揭示了现有基准的局限性并提出了更贴近真实场景的新基准，信息增量显著。
方法设计合理，实验覆盖多模型和智能体框架，结果可信，但缺乏对模型失败原因的深入分析。
为实际部署LLM进行数据分析提供了关键评估工具和改进方向，实用价值高。
论文发布时间为2025年7月，内容紧跟当前LLM应用热点，时效性极强。
来自arXiv预印本，尚未经同行评审，但方法透明且代码公开，可信度较好。
Base 88 × T2 Source weight ×0.9 = 79
Curation line 70Selected
Why selected
The paper proposes a well-designed real-world benchmark and reveals significant performance gaps through multi-model experiments, demonstrating strong novelty and practical value.
AI commentary
「What makes this paper particularly noteworthy is that it pops the bubble of LLM data analysis capabilities—models excel on simple benchmarks but struggle with multi-table, reasoning-intensive, and creative real-world data. For AI product managers, data scientists, and model developers, DataGovBench is a highly valuable testbed. Whoever achieves breakthroughs on this benchmark will gain a significant edge in next-generation intelligent data analysis tools.」
Quick overview
Current benchmarks for evaluating LLMs in data analysis often fail to reflect real-world settings, focusing on fact retrieval from small tables while ignoring multi-table datasets, external knowledge integration, and exploratory insight discovery. This paper introduces DataGovBench, a benchmark from governmental open data, with Table QA and Table Insight tasks. Experiments show significant performance gaps in state-of-the-art LLMs, even with agentic frameworks, indicating they are far from meeting real-world data analytics demands. Code and data are publicly available.
- Current data analysis benchmarks are too simplistic and fail to reflect real-world complexity like multi-table datasets, external knowledge, and exploratory analysis.
- DataGovBench is introduced with two tasks: Table QA (complex decomposable questions with textual/visual answers) and Table Insight (generating expert-level findings).
- Comprehensive experiments show significant performance gaps in state-of-the-art LLMs, even with agentic frameworks.
- The results highlight severe shortcomings of LLMs in realistic data analysis, providing a challenging benchmark for future research.
Full text
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