A Novel LLM enabled Code Snippet Generation Framework

Large Language Models (LLMs) represent a breakthrough in natural language processing (NLP), leveraging deep learning techniques to achieve exceptional proficiency in code generation, analysis and modification of human languages. These models, characterized by their vast scale and parameter count, like Bidirectional Encoder Representations from Transformers (BERT by Google) and the Generative Pre-trained Transformer series (by OpenAI’s GPT), have revolutionized various applications including text generation, translation, summarization, and question answering. In our paper we investigate the practicality,complications, and significance of using LLMs for code generation. We provide a review analysis of existing LLM models in use and compare their proficiency for code generation. This paper examines the underlying mechanisms of LLMs, specially their ability to grasp the code syntax, semantics, and programming logic from large-scale repositories and their documentations. The models’ training techniques include fine-tuning programming-specific datasets and enhancing the models' competency to generate code snippets that are syntactically correct and contextually relevant.