De-duplicating and De-commenting Code: Efficient Techniques for Improved Analytics

In this article, we delve into the realm of code generation and its potential applications in various fields. We explore how large-scale language models (LLMs) have become a game-changer in this area, offering solutions to basic programming problems and even tackling complex challenges. These models are based on the Transformer architecture and treat natural language descriptions as sequential transformation tasks.
The article begins by highlighting the significance of code capability, which is essential for enhancing key skills such as inference and planning in artificial intelligence (AI) models. We then examine how LLMs have made significant advancements in code generation, establishing themselves as the foundational backbone for a wide range of downstream tasks.
To illustrate their capabilities, we provide examples of code-related tasks that LLMs can handle with ease, such as generating code from natural language descriptions and solving basic programming problems. However, these models encounter substantial difficulties when tackling complex and novel programming challenges, which highlights the need for further research in this area.
To address these limitations, the article proposes several strategies, including treating the natural language description as a sequential transformation task and leveraging multitask learning techniques to improve the performance of LLMs in code generation. We also discuss the importance of excising comments from the code submissions retrieved from programming contest platforms, which can inflate the code’s length and introduce potential noise into subsequent analyses.
In conclusion, the article demonstrates that LLMs have immense potential in enhancing code generation capabilities, but there are still significant challenges to overcome. By leveraging sequential transformation tasks and multitask learning techniques, we can unlock the full potential of these models and revolutionize the field of code generation.