How does the performance gap between Code Llama Python and the general foundation model vary across specific D

Abstract

Abstract: Task automation has been greatly empowered by the recent advances in Large Language Models (LLMs) via Python code, where the tasks ranging from software engineering development to general-purpose reasoning. While current benchmarks have shown that LLMs can solve tasks using programs like human developers, the majority of their evaluations are limited to short and self-contained algorithmic tasks or standalone function calls. Solving challenging and practical tasks requires the capability of utilizing diverse function calls as tools to efficiently implement functionalities like data analysis an

Research Question

How does the performance gap between Code Llama Python and the general foundation model vary across specific DS-1000 libraries such as pandas, numpy, and matplotlib?

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Provenance