Bridging gaps in code generation with large language models
Large Language Models (LLMs) are transforming natural language processing and extending their impact to code generation. This thesis evaluates both academic and industrial LLMs, focusing on their ability to generate pragmatic, functional code for non-standalone functions—a critical aspect of real-wo...
- Autores:
-
Osorio Cálad, Juan José
- Tipo de recurso:
- Trabajo de grado de pregrado
- Fecha de publicación:
- 2025
- Institución:
- Universidad de los Andes
- Repositorio:
- Séneca: repositorio Uniandes
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.uniandes.edu.co:1992/75375
- Acceso en línea:
- https://hdl.handle.net/1992/75375
- Palabra clave:
- Bridging the Industry–Academia Gap
Large Language Models
Model Evaluation
Code Generation
Ingeniería
- Rights
- openAccess
- License
- Attribution 4.0 International