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Enhancing Code Generation with NLP by International Journal of Innovative Science and Research Technology is a document available to read on EtoBox.
This paper investigates methods to enhance semantic reasoning in AI-based code generation models, focusing on techniques like prompt engineering and domain-specific fine-tuning. The study evaluates models such as CodeT5 and PLBART using datasets like MBPP and APPS, revealing that contextual awareness and structured prompting significantly improve code quality. Findings indicate that while CodeT5 generally outperforms PLBART, both models struggle with complex tasks, emphasizing the need for improved evaluati
- Author
- International Journal of Innovative Science and Research Technology
- Language
- EN