In code search, the Generation-Augmented Retrieval (GAR) framework, which generates
exemplar code snippets to augment queries, has emerged as a promising strategy to
address the principal challenge of modality misalignment between code snippets and
natural language queries, particularly with the demonstrated code generation capabilities of
Large Language Models (LLMs). Nevertheless, our preliminary investigations indicate that
the improvements conferred by such an LLM-augmented framework are somewhat …