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🧠 AI⚪ NeutralImportance 6/10
KramaBench: A Benchmark for AI Systems on Data-to-Insight Pipelines over Data Lakes
arXiv – CS AI|Eugenie Lai, Gerardo Vitagliano, Ziyu Zhang, Om Chabra, Sivaprasad Sudhir, Anna Zeng, Anton A. Zabreyko, Chenning Li, Ferdi Kossmann, Jialin Ding, Jun Chen, Markos Markakis, Matthew Russo, Weiyang Wang, Ziniu Wu, Michael J. Cafarella, Lei Cao, Samuel Madden, Tim Kraska|
🤖AI Summary
Researchers introduce KramaBench, a comprehensive benchmark testing AI systems' ability to execute end-to-end data processing pipelines on real-world data lakes. The study reveals significant limitations in current AI systems, with the best performing system achieving only 55% accuracy in full data-lake scenarios and leading LLMs implementing just 20% of individual data tasks correctly.
Key Takeaways
- →KramaBench contains 104 manually curated challenges across 1700 files, 24 data sources, and 6 domains to test AI pipeline capabilities.
- →Current AI systems struggle with end-to-end data processing, achieving maximum 55% accuracy in full data-lake settings.
- →Even with perfect data retrieval, AI system accuracy only reaches 62%, indicating fundamental implementation limitations.
- →Leading LLMs can identify 42% of important data tasks but successfully implement only 20% of them.
- →Multi-agent and single-agent AI systems both show significant gaps in complex data orchestration capabilities.
#ai-benchmarking#data-processing#machine-learning#llm-evaluation#data-lakes#ai-limitations#research#automation
Read Original →via arXiv – CS AI
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