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arXiv AI By Yuxuan Chen, Mingwei Liu, Guangsheng Ou, Zekai Zhang, Zike Li, Yanlin Wang, Pelin Zheng

MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning

Read the original on arXiv AI →

MAP4CS is a data pruning framework that selects a small, high‑quality core subset from large code corpora by combining syntactic structure, semantic diversity, and distributional representation, followed by rule‑based filtering. Experiments on two large datasets show that using only 5% of the training data, MAP4CS outperforms random sampling and matches or exceeds full‑corpus fine‑tuning, supporting the idea that less data can be more effective. The method also automatically removes duplicates and noise, producing a lexically diverse and information‑dense training set.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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