Abstract
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 identifies a small, high-quality core subset by integrating syntactic structure, semantic diversity, and distributional representation, followed by a rigorous rule-based filtering pipeline. Extensive experiments on two large-scale datasets demonstrate that MAP4CS consistently outperforms random sampling baselines using only 5% of the training data. Remarkably, it achieves performance comparable to, or even superior to, fine-tuning on the full dataset, validating the ''less is more'' hypothesis in data-centric AI. Furthermore, linguistic analysis reveals an adaptive optimization mechanism: MAP4CS automatically functions as a de-duplicator for redundant corpora and a denoiser for chaotic ones, constructing a training corpus that is both lexically diverse and information-dense.
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Publication details
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Cite this article
APA 7
Chen, Y., Liu, M., Ou, G., Zhang, Z., Li, Z., Wang, Y., & Zheng, P. (2026). MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning. https://omanscience.com/en/articles/map4cs-a-multi-dimensional-data-pruning-framework-for-efficient-code-retriever-fine-tuning
MLA 9
Chen, Yuxuan, et al. "MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning." https://omanscience.com/en/articles/map4cs-a-multi-dimensional-data-pruning-framework-for-efficient-code-retriever-fine-tuning.
Chicago (author–date)
Chen, Yuxuan, Mingwei Liu, Guangsheng Ou, Zekai Zhang, Zike Li, Yanlin Wang, and Pelin Zheng. 2026. "MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning." https://omanscience.com/en/articles/map4cs-a-multi-dimensional-data-pruning-framework-for-efficient-code-retriever-fine-tuning.
Harvard
Chen, Y., Liu, M., Ou, G., Zhang, Z., Li, Z., Wang, Y. and Zheng, P. (2026) 'MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning', Available at: https://omanscience.com/en/articles/map4cs-a-multi-dimensional-data-pruning-framework-for-efficient-code-retriever-fine-tuning.
Vancouver
Chen Y, Liu M, Ou G, Zhang Z, Li Z, Wang Y, et al. MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning. https://omanscience.com/en/articles/map4cs-a-multi-dimensional-data-pruning-framework-for-efficient-code-retriever-fine-tuning
IEEE
Y. Chen, M. Liu, G. Ou, Z. Zhang, Z. Li, Y. Wang, and P. Zheng, "MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning," https://omanscience.com/en/articles/map4cs-a-multi-dimensional-data-pruning-framework-for-efficient-code-retriever-fine-tuning.