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 and Large Vision-Language Models (LVLMs) have demonstrated impressive language%2Fvision reasoning abilities%2C igniting the recent trend of building agents for targeted applications such as shopping assistants or AI software engineers. Recently%2C many data science benchmarks have been proposed to investigate their performance in the data science domain. However%2C existing data science benchmarks still fall short when compared to real-world data science applications due to their simplified settings. To bridge this gap%2C we introduce DSBench%2C a comprehensive benchmark designed to evaluate data science agents with realistic tasks. This benchmark includes 466 data analysis tasks and 74 data modeling tasks%2C sourced from Eloquence and Kaggle competitions. DSBench offers a realistic setting by encompassing long contexts%2C multimodal task backgrounds%2C reasoning with large data files and multi-table structures%2C and performing end-to-end data modeling tasks. Our evaluation of state-of-the-art LLMs%2C LVLMs%2C and agents shows that they struggle with most tasks%2C with the best agent solving only 34.12%25 of data analysis tasks and achieving a 34.74%25 Relative Performance Gap (RPG). These findings underscore the need for further advancements in developing more practical%2C intelligent%2C and autonomous data science agents.?quality=80&w=800)
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