Full fine-tuning yields statistically different and more focused attribution patterns than LoRA or quantized LoRA, while larger models prioritize numerical constraints and rule identifiers but show performance plateaus beyond 7B parameters in semantic similarity for code compliance.
The attribution analysis compared fine-tuning strategies on only the LLaMA 3B model and model scales across the three designated models
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LLM attribution analysis across different fine-tuning strategies and model scales for automated code compliance
Full fine-tuning yields statistically different and more focused attribution patterns than LoRA or quantized LoRA, while larger models prioritize numerical constraints and rule identifiers but show performance plateaus beyond 7B parameters in semantic similarity for code compliance.