DataAgent: Evaluating Large Language Models' Ability to Answer Zero-Shot, Natural Language Queries

arXiv:2404.00188v1 Announce Type: new
Abstract: Conventional processes for analyzing datasets and extracting meaningful information are often time-consuming and laborious. Previous work has identified manual, repetitive coding and data collection as major obstacles that hinder data scientists from undertaking more nuanced labor and high-level projects. To combat this, we evaluated OpenAI's GPT-3.5 as a "Language Data Scientist" (LDS) that can extrapolate key findings, including correlations and basic information, from a given dataset. The model was tested on a diverse set of …

abstract arxiv coding collection cs.ai cs.cl data data collection data scientists datasets hinder information labor language language models large language large language models major natural natural language natural language queries obstacles processes projects queries scientists type work zero-shot