[2603.23047] Parametric Knowledge and Retrieval Behavior in RAG Fine-Tuning for Electronic Design Automation

Computer Science > Computation and Language arXiv:2603.23047 (cs) [Submitted on 24 Mar 2026] Title:Parametric Knowledge and Retrieval Behavior in RAG Fine-Tuning for Electronic Design Automation Authors:Julian Oestreich, Maximilian Bley, Frank Binder, Lydia Müller, Maksym Sydorenko, André Alcalde View a PDF of the paper titled Parametric Knowledge and Retrieval Behavior in RAG Fine-Tuning for Electronic Design Automation, by Julian Oestreich and Maximilian Bley and Frank Binder and Lydia M\"uller and Maksym Sydorenko and Andr\'e Alcalde View PDF HTML (experimental) Abstract: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 introduce TriFEX, a human-validated, triple-based evaluation pipeline that attributes generated claims to their origin-user query, context and reference-and propose Parametric Knowledge Precision (PKP), which isolates internalized knowledge by filtering out claims leaked in the prompt. We show that ROUGE and BERTScore fail to detect factual differences that our triple-based evaluation reveals. Additionally, we demonstrate that an existing metric for knowledge internalization is retrieva-s...