Enhancing Parameter-Efficient Code Representations with Retrieval and Structural Priors

Overview

This work addresses parameter-efficient fine-tuning (PEFT) of code pre-trained models (CodePTMs) for code representation learning. The study identifies two primary limitations in existing PEFT approaches: insufficient capture of deep structural characteristics in programs and knowledge bottlenecks inherent to finite model parameters. The research proposes a framework integrating retrieval augmentation with structure-aware mechanisms to enhance code representations while maintaining computational efficiency.

Methods and approach

The proposed framework comprises three lightweight, complementary modules designed to augment parameter-efficient code representations. A structure-semantic dual-channel retrieval mechanism incorporates external code knowledge as non-parametric memory to mitigate knowledge bottleneck constraints. A graph relative bias module enhances the attention mechanism's ability to model structural program relationships through graph-informed attention patterns. A span-discriminative contrastive objective refines span-level representation distinctiveness and boundary clarity through contrastive learning. These modules operate within the PEFT paradigm, utilizing only approximately 5% of standard fine-tuning parameters while building upon CodePTM foundations.

Results

Experimental evaluation across three benchmarks spanning six programming languages demonstrates consistent improvements over state-of-the-art parameter-efficient baselines. On structure-sensitive tasks using the PLBART backbone, the method achieves 22.1% improvement in Exact Match for code generation and 4.4% increase in BLEU scores for code refinement. Notably, these results surpass full fine-tuning performance while employing only about 5% of the trainable parameters required by standard fine-tuning approaches, indicating substantial parameter efficiency gains without performance degradation.

Implications

The integration of retrieval augmentation and structural priors provides a methodological approach to addressing knowledge bottleneck limitations in parameter-efficient model adaptation. The demonstrated superiority over full fine-tuning while using minimal trainable parameters suggests that external knowledge integration and structural inductive biases may provide more efficient optimization pathways than parameter expansion. This framework contributes to the field of parameter-efficient adaptation for code intelligence tasks, particularly for structure-sensitive code understanding and generation objectives.