MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning
Retrieval-Augmented Generation (RAG) has become a cornerstone in software engineering for enhancing Large Language Models (LLMs) with domain-specific knowledge. However, adapting retrievers to evolving code repositories remains challenging due to the noise and redundancy inherent in massive code corpora. Standard fine-tuning on the full corpus is computationally expensive and often leads to sub-optimal performance due to negative transfer from low-quality samples. Conversely, simple random sampling fails to guarantee data representativeness. To address these challenges, we propose MAP4CS (Multi-dimensional Awareness Pruning for Code Search), an adaptive data pruning framework. MAP4CS iden...
| Authors | Yuxuan Chen, Mingwei Liu, Guangsheng Ou et al. |
|---|---|
| Primary category | cs.SE |
| Published | 8 October 2026 |
| Last updated at source | 8 October 2026 |
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