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.
and Kim, D.: Performance comparison of retrieval-augmented generation and fine-tuned large language models for construction safety management knowledge retrieval
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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.