arXiv:2607.01647v1 Announce Type: cross Abstract: Data science aims to derive actionable insights from heterogeneous raw data, unlocking the value of the massive amounts of data generated in modern society. Automating this process is essential to reducing labor-intensive efforts for data scientists and enabling scalable data-driven applications. Recently, large language model (LLM)-based data agents have emerged as a promising solution to automate data science workflows. However, the field lacks comprehensive benchmarks to rigorously evaluate these agents across diverse scenarios with fine-gra
The proliferation of Large Language Models (LLMs) and their application in data science workflows has created a natural demand for robust evaluation frameworks like AgenticDataBench.
A comprehensive benchmark for data agents is crucial for validating the efficacy and reliability of autonomous systems designed to automate white-collar tasks, impacting industries reliant on data analysis.
The introduction of a standardized benchmark will enable more rigorous, objective evaluation of AI agents, accelerating their development and adoption while separating performant solutions from less reliable ones.
- · AI agent developers
- · Data scientists
- · Enterprises adopting AI agents
- · AI research institutions
- · Ineffective AI agent solutions
- · Companies relying on manual data processing
The benchmark provides a common ground for comparing and improving LLM-based data agents.
Accelerated development of efficient data agents will lead to further automation of data science workflows, reducing labor costs and increasing analytical output.
Successful deployment of these agents across various industries could fundamentally alter the demand for human data scientists, shifting roles towards oversight and specialized problem-solving.
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Read at arXiv cs.CL