VRank selects better LLM-generated Verilog code by clustering candidates that produce identical simulation outputs and ranking the clusters by consistency, yielding an average 10.5% pass@1 gain on VerilogEval-Human.
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VRank: Enhancing Verilog Code Generation from Large Language Models via Self-Consistency
VRank selects better LLM-generated Verilog code by clustering candidates that produce identical simulation outputs and ranking the clusters by consistency, yielding an average 10.5% pass@1 gain on VerilogEval-Human.