Parametric Knowledge and Retrieval Behavior in RAG Fine-Tuning for Electronic Design Automation

ArXi:2603.23047v1 Announce Type: cross Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering and often rely on standard NLP metrics that can obscure factual differences. We evaluate RAG fine-tuning for long-form text generation in electronic design automation, adapting a 7B model under five context augmentation strategies with varying retrieval conditions. We.