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

Key points

• Retrieval-Augmented Generation (RAG) has become a cornerstone in software engineering for enhancing Large Language Models (LLMs) with domain-specific knowledge.1
• However, adapting retrievers to evolving code repositories remains challenging due to the noise and redundancy inherent in massive code corpora.1
• 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.1
• 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.1

Sources (1)

• [1]MAP4CS: A Multi-dimensional Data Pruning Framework for Efficient Code Retriever Fine-tuning
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 8, 11:25 AM

  To address these challenges, we propose MAP4CS (Multi-dimensional Awareness Pruning for Code Search), an adaptive data pruning framework.

  Retrieval-Augmented Generation (RAG) has become a cornerstone in software engineering for enhancing Large Language Models (LLMs) with domain-specific knowledge.

Extractive summary: sentences quoted from the sources.