LoRACode: LoRA Adapters for Code Embeddings

• URL: http://arxiv.org/abs/2503.05315v1
• Date: Fri, 07 Mar 2025 10:50:45 GMT
• Title: LoRACode: LoRA Adapters for Code Embeddings
• Authors: Saumya Chaturvedi, Aman Chadha, Laurent Bindschaedler,
• Abstract summary: We introduce a parameter-efficient fine-tuning method based on Low-Rank Adaptation (LoRA) to construct task-specific adapters for code retrieval.<n>Our approach reduces the number of trainable parameters to less than two percent of the base model, enabling rapid fine-tuning on extensive code corpora.
• Score: 1.5525560291268214
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Code embeddings are essential for semantic code search; however, current approaches often struggle to capture the precise syntactic and contextual nuances inherent in code. Open-source models such as CodeBERT and UniXcoder exhibit limitations in scalability and efficiency, while high-performing proprietary systems impose substantial computational costs. We introduce a parameter-efficient fine-tuning method based on Low-Rank Adaptation (LoRA) to construct task-specific adapters for code retrieval. Our approach reduces the number of trainable parameters to less than two percent of the base model, enabling rapid fine-tuning on extensive code corpora (2 million samples in 25 minutes on two H100 GPUs). Experiments demonstrate an increase of up to 9.1% in Mean Reciprocal Rank (MRR) for Code2Code search, and up to 86.69% for Text2Code search tasks across multiple programming languages. Distinction in task-wise and language-wise adaptation helps explore the sensitivity of code retrieval for syntactical and linguistic variations.

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