RAG-adapted LLaMA-3-8B outperforms both baseline and fine-tuned models on expert-rated accuracy (75.5%), relevance (90.8%), and overall preference (85.2%) for additive manufacturing questions.
The results show that retrieval-augmented generation significantly improves model performance by g rounding responses in domain -specific knowledge
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Domain Adaptation of Large Language Models for Polymer-Composite Additive Manufacturing Using Retrieval-Augmented Generation and Fine-Tuning
RAG-adapted LLaMA-3-8B outperforms both baseline and fine-tuned models on expert-rated accuracy (75.5%), relevance (90.8%), and overall preference (85.2%) for additive manufacturing questions.