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

arXiv:2610.11727 · cs.SE, cs.AI · Submitted 2026-10-08 · Read on arXiv

cs.SE, cs.AI

Submitted: 2026-10-08

Updated: 2026-10-08

Terminology

• Data Pruning — 1× in this paper · explained in 2 episodes
• Fine-Tuning — 1× in this paper · explained in 5 episodes
• Pruning — 1× in this paper · explained in 4 episodes

Sources

• Combining Neural Architecture Search and Automatic Code Optimization: A Survey
• Are Decoder-Only Large Language Models the Silver Bullet for Code Search?
• MoDS: Model-oriented Data Selection for Instruction Tuning
• Vul-RAG: Enhancing LLM-based Vulnerability Detection via Knowledge-level RAG
• What to Retrieve for Effective Retrieval-Augmented Code Generation? An Empirical Study and Beyond
• Textbooks Are All You Need
• CodeSearchNet Challenge: Evaluating the State of Semantic Code Search
• Semantic Sensitive TF-IDF to Determine Word Relevance in Documents
• Scaling Laws for Neural Language Models
• StarCoder 2 and The Stack v2: The Next Generation
• Evolving Triple Knowledge-Augmented LLMs for Code Translation in Repository Context
• Qwen3 Embedding: Advancing Text Embedding and Reranking Through Foundation Models
• A Survey of Large Language Models for Code: Evolution, Benchmarking, and Future Trends

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