Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B

TL;DR

This paper evaluates Llama 3.1 405B's ability to generate code from natural language prompts, demonstrating its strengths in multi-language support and debugging, while identifying limitations in complex scientific domains.

Contribution

It provides a comprehensive analysis of Llama 3.1 405B's code generation capabilities and discusses its potential impact on software development and education.

Findings

Performs well on simple algorithmic problems

Supports multiple programming languages

Struggles with complex scientific problems like Quantum Computing

Abstract

Code generation by Llama 3.1 models, such as Meta's Llama 3.1 405B, represents a significant advancement in the field of artificial intelligence, particularly in natural language processing and programming automation. This paper explores the capabilities and applications of Llama-driven code generation, highlighting its ability to translate natural language prompts into executable code across multiple programming languages. Key features include contextual awareness, multi-language support, and enhanced debugging and optimization functionalities. By examining these aspects, we illustrate how Llama can serve as a versatile tool for developers of all skill levels, improving productivity and efficiency in software development. The potential implications for education, industry, and the future of coding practices are also discussed, underscoring the transformative impact of AI in programming. Experimentation shows that while Llama 3.1 405B performs well with simple algorithmic and data structure based problems, it still struggles with problems on Quantum Computing, Bioinformatics, and Artificial Intelligence.