What is the performance gap in code generation tasks (HumanEval) between multimodal models and text-only LLMs

Abstract

Abstract: We release Code Llama, a family of large language models for code based on Llama 2 providing state-of-the-art performance among open models, infilling capabilities, support for large input contexts, and zero-shot instruction following ability for programming tasks. We provide multiple flavors to cover a wide range of applications: foundation models (Code Llama), Python specializations (Code Llama - Python), and instruction-following models (Code Llama - Instruct) with 7B, 13B, 34B and 70B parameters each. All models are trained on sequences of 16k tokens and show improvements on inputs with up

Research Question

What is the performance gap in code generation tasks (HumanEval) between multimodal models and text-only LLMs when provided with additional visual context or pseudocode diagrams?

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