arXiv (All)
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Quality over Quantity: Diversity-Aware Data Selection for Efficient Verilog Code Generation
Large Language Models (LLMs) have shown remarkable potential in Verilog code generation, yet existing datasets contain con siderable noise and redundancy. Prior data selection methods address only isolated quality aspects, neglect the global diversity of the training set, and cannot capture Verilog-
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