VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination

• URL: http://arxiv.org/abs/2503.13572v2
• Date: Mon, 14 Apr 2025 10:02:07 GMT
• Title: VeriContaminated: Assessing LLM-Driven Verilog Coding for Data Contamination
• Authors: Zeng Wang, Minghao Shao, Jitendra Bhandari, Likhitha Mankali, Ramesh Karri, Ozgur Sinanoglu, Muhammad Shafique, Johann Knechtel,
• Abstract summary: Large Language Models (LLMs) have revolutionized code generation, achieving exceptional results on various established benchmarking frameworks.<n>However, concerns about data contamination raise questions about the validity of these evaluations.<n>We analyze state-of-the-art (SOTA) evaluation frameworks for Verilog code generation.
• Score: 15.52442661491358
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Large Language Models (LLMs) have revolutionized code generation, achieving exceptional results on various established benchmarking frameworks. However, concerns about data contamination - where benchmark data inadvertently leaks into pre-training or fine-tuning datasets - raise questions about the validity of these evaluations. While this issue is known, limiting the industrial adoption of LLM-driven software engineering, hardware coding has received little to no attention regarding these risks. For the first time, we analyze state-of-the-art (SOTA) evaluation frameworks for Verilog code generation (VerilogEval and RTLLM), using established methods for contamination detection (CCD and Min-K% Prob). We cover SOTA commercial and open-source LLMs (CodeGen2.5, Minitron 4b, Mistral 7b, phi-4 mini, LLaMA-{1,2,3.1}, GPT-{2,3.5,4o}, Deepseek-Coder, and CodeQwen 1.5), in baseline and fine-tuned models (RTLCoder and Verigen). Our study confirms that data contamination is a critical concern. We explore mitigations and the resulting trade-offs for code quality vs fairness (i.e., reducing contamination toward unbiased benchmarking).

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