Large language models (LLMs) have shown remarkable capabilities in code generation.
However, the effects of hallucinations (eg, output noise) make it particularly challenging for
LLMs to generate high-quality code in one pass. In this work, we propose a simple and
effective uncertainty-aware selective contrastive decoding (USCD) mechanism to improve
the quality of one-pass code generation in LLMs and reduce the impact of output noise. To
be specific, we first elaborately designed a negative prompt (namely lame prompt) to output …

Large language models (LLMs) have shown remarkable capabilities in code generation.
However, the effects of hallucinations (eg, output noise) make it particularly challenging for
LLMs to generate high-quality code in one pass. In this work, we propose a simple and
effective\textbf {u} ncertainty-aware\textbf {s} elective\textbf {c} ontrastive\textbf {d} ecoding
($\mathbb {USCD} $) mechanism to improve the quality of one-pass code generation in
LLMs and reduce the impact of output noise. To be specific, we first elaborately designed a …