Large Language Models (LLMs) have significantly impacted numerous domains, notably
including Software Engineering (SE). Nevertheless, a well-rounded understanding of the …

The advent of large language models trained on code (code LLMs) has led to significant
progress in language-to-code generation. State-of-the-art approaches in this area combine …

Although Large Language Models (LLMs) have demonstrated remarkable code-generation
ability, they still struggle with complex tasks. In real-world software development, humans …

Finetuning large language models (LLMs) on instructions leads to vast performance
improvements on natural language tasks. We apply instruction tuning using code …

Although large language models (LLMs) have demonstrated impressive ability in code
generation, they are still struggling to address the complicated intent provided by humans. It …

Breaking down a problem into intermediate steps has demonstrated impressive
performance in Large Language Model (LLM) reasoning. However, the growth of the …

Large language models (LLMs) have demonstrated an impressive ability to generate codes
on competitive programming tasks. However, with limited sample numbers, LLMs still suffer …

Large Language Models (LLMs) have shown remarkable aptitude in code generation but
still struggle on challenging programming tasks. Self-repair--in which the model debugs and …

Inductive reasoning is a core problem-solving capacity: humans can identify underlying
principles from a few examples, which can then be robustly generalized to novel scenarios …

Despite recent success in large language model (LLM) reasoning, LLMs struggle with
hierarchical multi-step reasoning tasks like generating complex programs. For these tasks …