# Parametric Knowledge in RAG-SFT for Domain-Specific Document Generation

arXiv: https://arxiv.org/abs/2603.23047 · HTML: https://mlsanity.com/paper/2603.23047/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in RAG pipeline design and evaluation, one of 15 directions in Retrieval-augmented generation.
- Builds on 16 of its 27 references that are in the map.
- First posted on arXiv 24 Mar 2026.

## Placed in

- Retrieval-augmented generation / [RAG pipeline design and evaluation](https://mlsanity.com/rag/rag-pipeline-design-and-evaluation/)

## Builds on (most cited first, 16 of 16)

- [BERTScore: Evaluating Text Generation with BERT](https://mlsanity.com/paper/1904.09675/) (2019)
- [FEVER: a large-scale dataset for Fact Extraction and VERification](https://mlsanity.com/paper/1803.05355/) (2018)
- [FActScore: Fine-grained Atomic Evaluation of Factual Precision in Long Form Text Generation](https://mlsanity.com/paper/2305.14251/) (2023)
- [Making Retrieval-Augmented Language Models Robust to Irrelevant Context](https://mlsanity.com/paper/2310.01558/) (2023)
- [RAFT: Adapting Language Model to Domain Specific RAG](https://mlsanity.com/paper/2403.10131/) (2024)
- [Fine-tuning Language Models for Factuality](https://mlsanity.com/paper/2311.08401/) (2023)
- [Fine-Tuning or Retrieval? Comparing Knowledge Injection in LLMs](https://mlsanity.com/paper/2312.05934/) (2023)
- [ChatQA: Surpassing GPT-4 on Conversational QA and RAG](https://mlsanity.com/paper/2401.10225/) (2024)
- [ChatQA 2: Bridging the Gap to Proprietary LLMs in Long Context and RAG Capabilities](https://mlsanity.com/paper/2407.14482/) (2024)
- [Global Explainability of BERT-Based Evaluation Metrics by Disentangling along Linguistic Factors](https://mlsanity.com/paper/2110.04399/) (2021)
- [Efficient Knowledge Injection in LLMs via Self-Distillation](https://mlsanity.com/paper/2412.14964/) (2024)
- [The Great Nugget Recall: Automating Fact Extraction and RAG Evaluation with Large Language Models](https://mlsanity.com/paper/2504.15068/) (2025)
- [Beyond Factual Accuracy: Evaluating Coverage of Diverse Factual Information in Long-form Text Generation](https://mlsanity.com/paper/2501.03545/) (2025)
- [Resisting Contextual Interference in RAG via Parametric-Knowledge Reinforcement](https://mlsanity.com/paper/2506.05154/) (2025)
- [Systematic Knowledge Injection into Large Language Models via Diverse Augmentation for Domain-Specific RAG](https://mlsanity.com/paper/2502.08356/) (2025)
- [Towards Better Evaluation for Generated Patent Claims](https://mlsanity.com/paper/2505.11095/) (2025)

## Abstract (arXiv)

Retrieval-Augmented Generation (RAG) fine-tuning has shown substantial improvements over vanilla RAG, yet most studies target document question answering, leaving open whether these gains transfer to specialized tasks. We study supervised RAG fine-tuning (RAG-SFT) for requirements document generation in the electronics engineering domain, adapting two 7B models under two different training data strategies. Because Rouge and BertScore poorly capture factuality on long technical text, we introduce C-FEX, a claim-based evaluation pipeline that attributes each response claim to its origin (augmented prompt or reference response), and propose Parametric Knowledge Precision (PKP), which isolates claims originating from the model's weights and measures their correctness. We show that a prior metric to assess parametric knowledge decomposes as PKP × PR, separating the rate of parametric output (PR) from its quality (PKP). Empirically, fine-tuned 7B models match or exceed a 72B baseline; standard metrics disagree with claim-based factuality and can mislead about fine-tuning gains; and, fine-tuning does not reinforce correct parametric knowledge but suppresses hallucination---models speak from their weights less often but far more reliably.