The increasing development of large language models (LLMs) in code generation has
drawn significant attention among researchers. To enhance LLM-based code generation 

Large language models (LLMs) have shown impressive effectiveness in various software
engineering tasks, including automated program repair (APR). In this study, we take a deep 

The advancement of large language models (LLMs) has significantly propelled the field of
code generation. Previous work integrated reinforcement learning (RL) with compiler 

Large Language Models (LLMs) exhibit remarkably powerful capabilities. One of the crucial
factors to achieve success is aligning the LLM's output with human preferences. This 

Transformers have revolutionized natural language processing (NLP) and have had a huge
impact on automating tasks. Recently, transformers have led to the development of powerful 

In recent years, Large Language Models (LLMs) have achieved remarkable success and
have been widely used in various downstream tasks, especially in the tasks of the software 

While large language models have made significant strides in code generation, the pass
rate of the generated code is bottlenecked on subtle errors, often requiring human 

Neural Code Intelligence--leveraging deep learning to understand, generate, and optimize
code--holds immense potential for transformative impacts on the whole society. Bridging the 

JavaScript interpreters, crucial for modern web browsers, require an effective fuzzing
method to identify security-related bugs. However, the strict grammatical requirements for 

Large Language Models (LLMs) have garnered remarkable advancements across diverse
code-related tasks, known as Code LLMs, particularly in code generation that generates