arXiv:2601.13591v2 Announce Type: replace-cross Abstract: Recent LLM-based data agents aim to automate data science tasks ranging from data analysis to deep learning. However, the open-ended nature of real-world data science problems, which often span multiple taxonomies and lack standard answers, poses a significant challenge for evaluation. To address this, we introduce DSAEval, a benchmark comprising 641 real-world data science problems grounded in 285 diverse datasets, covering both structured and unstructured data (e.g., image and text). DSAEval incorporates three distinctive features: (1
The proliferation of LLM-based agents necessitates robust and standardized evaluation methods to ensure their practical utility and accelerate development in real-world data science problems.
A standardized benchmark for data science agents is crucial for comparing agent performance, identifying limitations, and driving the advancement of autonomous AI systems capable of complex problem-solving.
The introduction of DSAEval provides a comprehensive framework for evaluating AI agents on real-world data science tasks, shifting from anecdotal evidence to quantifiable performance metrics.
- · AI agent developers
- · Data science platforms
- · Enterprises adopting AI agents
- · Academic AI researchers
- · AI agent developers with poor evaluation methods
- · Manual data science service providers
DSAEval will accelerate the development and performance of AI agents in data science.
Improved AI agents will lead to greater automation of data analysis and machine learning workflows, impacting white-collar employment.
The widespread adoption of highly capable data science agents could reshape economic productivity models and the demand for human data scientists.
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Read at arXiv cs.CL