Benchmarking Data Science Agents
Abstrak
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 as a series of innovative benchmarks tailored for assessing the performance of these agents throughout the entire data science lifecycle. Incorporating a novel bootstrapped annotation method, we streamline dataset preparation, improve the evaluation coverage, and expand benchmarking comprehensiveness. Our findings uncover prevalent obstacles and provide critical insights to inform future advancements in the field.
Topik & Kata Kunci
Penulis (6)
Yuge Zhang
Qiyang Jiang
Xingyu Han
Nan Chen
Yuqing Yang
Kan Ren
Akses Cepat
- Tahun Terbit
- 2024
- Bahasa
- en
- Total Sitasi
- 37×
- Sumber Database
- Semantic Scholar
- DOI
- 10.48550/arXiv.2402.17168
- Akses
- Open Access ✓