Benchmarking Data Science Agents

arXiv:2402.17168v1 Announce Type: cross
Abstract: In the era of data-driven decision-making, the complexity of data analysis necessitates advanced expertise and tools of data science, presenting significant challenges even for specialists. Large Language Models (LLMs) have emerged as promising aids as data science agents, assisting humans in data analysis and processing. Yet their practical efficacy remains constrained by the varied demands of real-world applications and complicated analytical process. In this paper, we introduce DSEval -- a novel evaluation paradigm, as well …

abstract advanced agents analysis arxiv benchmarking challenges complexity cs.ai cs.cl data data analysis data-driven data science decision expertise humans language language models large language large language models llms making practical presenting processing science tools type