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Code Generation and Algorithmic Problem Solving Using Llama 3.1 405B

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arxiv 2409.19027 v2 pith:DPJM3JM7 submitted 2024-09-26 cs.CL cs.SE

Aniket Deroy , Subhankar Maity This is my paper · ORCID
classification cs.CLcs.SE
keywords llamacodegenerationprogrammingalgorithmicartificialintelligencelanguage
verification ladder T0 review T1 audit T2 compute T3 formal
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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.

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Cited by 2 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. Full citation record

  1. Investigating the Role of LLMs Hyperparameter Tuning and Prompt Engineering to Support Domain Modeling

    cs.SE 2025-07 conditional novelty 5.0 of 10

    Combining search-based hyperparameter tuning of Llama 3.1 with few-shot prompting improves domain-model generation quality over default settings on most of ten test domains, though only for a text-similarity metric.

  2. Smaller = Weaker? Benchmarking Robustness of Quantized LLMs in Code Generation

    cs.SE 2025-06 reject novelty 5.0 of 10

    Quantized code LLMs appear more robust than full-precision ones in a majority of tested adversarial and noise scenarios, but the proposed Relative Robustness Score is misspecified.

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