Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities
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
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- PossiblePossibly related (embedding) · 54%thu-pacman/chitu →
- PossiblePossibly related (embedding) · 52%chrisliu298/awesome-llm-unlearning →
- PossiblePossibly related (embedding) · 52%douglasjordan2/c0 →
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- LinkedLinked via arxiv author · 85%So Hasegawa →
“Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities”
- LinkedLinked via arxiv author · 85%Shailaja Keyur Sampat →
“Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities”
- LinkedLinked via arxiv author · 85%Lei Liu →
“Data Analysis in the Wild: Benchmarking Large Language Models Against Real-World Data Complexities”