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arXiv · 2404.00188

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

Manit Mishra·Abderrahman Braham·Charles Marsom·Bryan Chung·Gavin Griffin·Dakshesh Sidnerlikar·Chatanya Sarin·Arjun Rajaram
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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 benchmark datasets to evaluate its performance across multiple standards, including data science code-generation based tasks involving libraries such as NumPy, Pandas, Scikit-Learn, and TensorFlow, and was broadly successful in correctly answering a given data science query related to the benchmark dataset. The LDS used various novel prompt engineering techniques to effectively answer a given question, including Chain-of-Thought reinforcement and SayCan prompt engineering. Our findings demonstrate great potential for leveraging Large Language Models for low-level, zero-shot data analysis.

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Manit Mishra, Abderrahman Braham, Charles Marsom, Bryan Chung, Gavin Griffin, Dakshesh Sidnerlikar, Chatanya Sarin, Arjun Rajaram. 2024-03-29. DataAgent: Evaluating Large Language Models' Ability to Answer Zero-Shot, Natural Language Queries. https://doi.org/10.1109/icaic60265.2024.10433803

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