Self-Infilling Decoding for Language Generation

In this research paper, the authors aim to enhance the capabilities of large language models (LLMs) by developing a framework for self-infilling code generation. They build upon recent successes in code generation and understanding tasks and focus on infilling content considering both preceding and subsequent contexts. The proposed framework is tested on various code-related tasks, demonstrating its effectiveness in generating coherent and accurate code.
The authors explain that LLMs have achieved remarkable results in code-related tasks, but their ability to generate full codes is limited. To address this issue, they propose a self-infilling framework that enables LLMs to generate missing content based on the context provided. The proposed framework uses a combination of reinforcement learning and natural language processing techniques to learn how to infill code content.
The authors introduce several key techniques in their framework, including interruption and looping. Interruptions enable the model to generate content based on partial input, while loops allow for multiple iterations of context-dependent generation. They also discuss the importance of considering both preceding and subsequent contexts when generating code.
The authors evaluate their framework on various code-related tasks, such as infilling missing functions and generating complete codes from incomplete inputs. The results show that their approach outperforms existing methods in terms of accuracy and efficiency.
The authors highlight several potential directions for future research, including exploring the application of their framework to other domains, developing more efficient algorithms, and incorporating structured output generation techniques. They also acknowledge the limitations of their approach, such as the need for additional computational resources for looping operations.
In summary, the article presents a novel self-infilling framework for enhancing the capabilities of LLMs in code generation tasks. The proposed approach leverages reinforcement learning and natural language processing techniques to generate complete and accurate codes based on contextual information. The authors demonstrate the effectiveness of their framework through extensive experiments and highlight potential areas for future research.