Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
arXiv:2607.06482v1 Announce Type: cross Abstract: 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
The rapid advancement of Large Language Models has necessitated more robust and realistic evaluation benchmarks to understand their capabilities in complex, real-world data analysis tasks.
Accurate benchmarking against real-world data complexities will reveal true LLM limitations and potential, guiding development towards more practical and reliable AI solutions for enterprise and government.
Our understanding of LLM performance in data analysis shifts from theoretical effectiveness on simple datasets to practical utility in complex, multi-tabular, and externally-reliant scenarios.
- · LLM developers focused on real-world application
- · Data scientists leveraging LLMs
- · Organizations with complex data analysis needs
- · AI-driven analytics platforms
- · LLMs optimized only for simple benchmarks
- · Traditional data analysis software unable to integrate LLMs effectively
The DataGovBench will become a standard for evaluating LLMs on practical data analysis.
Improved LLMs, trained and refined against such benchmarks, will accelerate automation of complex analytical workflows.
Government agencies and large enterprises will see increased adoption of LLM-powered data analysis for policy and business intelligence.
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Read at arXiv cs.AI