DistilledPRAG: Privacy-Preserving RAG

• DistilledPRAG is a privacy-preserving retrieval-augmented generation framework that uses knowledge distillation to align masked document representations with a standard RAG model.
• It synthesizes QA pairs and employs a parameter generator to convert masked document embeddings into LoRA parameters, facilitating secure and efficient inference.
• The framework achieves up to 6.8% accuracy improvement and robust out-of-domain generalization, ensuring precise QA without exposing plaintext data.

DistilledPRAG is defined as a privacy-preserving retrieval-augmented generation framework that leverages knowledge distillation to align parametric document representations with the behavior of standard RAG (Retrieval Augmented Generation). It is designed to avoid uploading plaintext documents to the cloud, preventing information leakage, while maintaining high reasoning performance and generalization on question answering tasks. Technically, DistilledPRAG synthesizes QA pairs, converts masked document representations into LoRA (Low-Rank Adaptation) parameters using a parameter generator, and distills both hidden state and output logit distributions by matching them with a standard RAG teacher model. It ensures privacy by masking document content, yet retains the standard RAG document structure and activation patterns through knowledge distillation. This approach yields improved accuracy and robust out-of-distribution generalization.

1. Motivation and Comparison with Parametric RAG Systems

DistilledPRAG addresses two central limitations of previous parametric RAG systems:

• Inference latency: PRAG typically requires the synthesis of QA pairs and individual fine-tuning for each document to obtain LoRA adaptations, resulting in slow inference.
• Generalization and alignment: Naive PRAG relies only on synthetic QA data without structural or activation alignment to standard RAG, leading to poor robustness for OOD queries.

In contrast, DistilledPRAG introduces a parameter generator architecture that produces unified, cross-document LoRA representations. Instead of independently fine-tuning for each document, masked representations are encoded and then mapped to LoRA parameters via learned feed-forward networks. The model is directly trained to match the hidden states and output logits of a reference RAG system, enforcing internal alignment that supports RAG-consistent reasoning.

2. Technical Framework and Parameter Generation

QA Synthesis and Training Corpus

Synthetic QA pairs are produced for both individual documents and concatenated multi-document inputs using a model such as DeepSeek-V3. This dual augmentation supports intra-document and cross-document reasoning. The training set comprises diverse QA examples, representing the full complexity of the reasoning space required for factual and multi-hop QA tasks.

Document Masking and LoRA Generation

During training and inference, document tokens are replaced by a learned mask (<|doc_mask|>), which is initialized such that its embedding is statistically consistent with pretrained vocabulary embeddings:

$$e_{mask} = M + E, \quad E \sim N(0, \text{diag}(\sigma^2))$$

A parameter generator, built on the LongT5 encoder backbone, processes the masked document to produce document embeddings $E_{D_i}$. These embeddings are fed through multi-head cross-attention and self-attention layers followed by an FFN to yield LoRA weights:

$$H_{0} = \mathrm{CrossAttention}(Q, E_{D_i}, E_{D_i}) \ H_{1} = \mathrm{SelfAttentionEncoder}(H_{0}) \ T_{o}(D_i) = \mathrm{FFN}(H_{1})$$

The input format mimics standard RAG by concatenating masked documents and queries even though the actual content is replaced, maintaining compatibility with downstream processes.

Distillation Objectives

The training objective combines losses over generated QA output, internal hidden states, and output logits compared to a RAG teacher:

$$\min_{w} \mathbb{E}_{(D_i, q_i, a_i) \sim D} \left[ L_{gen} + \lambda_{1} L_{cos} + \lambda_{2} L_{KL} \right]$$

Where $L_{gen}$ is the QA generation loss, $L_{cos}$ matches hidden states, and $L_{KL}$ matches output logits distributions.

3. Privacy Mechanisms and Security Guarantees

DistilledPRAG achieves privacy by:

• Thoroughly masking each document’s token with <|doc_mask|>, ensuring the raw text is never transmitted.
• Using a carefully initialized embedding for the mask token, preserving activation distributions and stability.
• Training the parameter generator strictly on masked inputs and synthetic data, so that inference never necessitates document decryption or plaintext upload.

The entire framework is designed to operate within the constraints of strong privacy and is suitable for cloud deployment scenarios where document confidentiality is critical.

4. Performance and Generalization

Comprehensive validations reported in the underlying paper use four QA benchmarks (2WikiMultihopQA, HotpotQA, PopQA, Complex WebQuestions) and demonstrate:

• Superior or competitive F1 scores compared to baselines: DistilledPRAG achieves accuracy improvements up to 6.8% over standard RAG and larger margins over previous PRAG variants.
• Consistent generalization performance, despite training on one QA dataset, on OOD benchmarks—attributed to effective internal alignment of hidden states and output logits and to multi-document QA augmentation.

The system generalizes robustly to novel domains and queries even when they are substantially different from training distributions.

5. Document Structure and Reasoning Consistency

Contrary to earlier PRAG and DyPRAG models that aggregate LoRA parameters (via concatenation or averaging) for individual documents, DistilledPRAG maintains the canonical RAG input structure: retrieved documents concatenated and passed with a query. Its parameter generator outputs a set of LoRA weights associated with the masked document input, activating the same internal pathways and output distributions as standard RAG.

The approach is validated by comparing not only QA output performance, but also the match between the student's and teacher's intermediate representations.

6. Limitations and Future Directions

Several open challenges remain:

• There is an approximation gap between parametric LoRA-based RAG and the teacher reference RAG, particularly for nuanced, multi-document questions. While internal alignment narrows this gap, cross-document interactions are not perfectly preserved.
• Privacy masking by necessity introduces some semantic loss; trade-offs between privacy and utility may arise in knowledge-intensive scenarios.
• Future research is anticipated to address richer alignment objectives, develop more expressive parameter generators, and extend the paradigm to multi-modal reasoning and generative tasks.

Potential advances include tighter activation alignment and broader applicability to heterogeneous document sets.

7. Applications and Significance

DistilledPRAG provides an effective privacy-preserving alternative to conventional cloud-based RAG systems. Its capacity to support accurate QA without exposing plaintext data, robust generalization to out-of-domain queries, and efficient inference latency makes it suitable for deployment in contexts where confidentiality and performance are imperative—for instance, in healthcare, finance, or enterprise document retrieval pipelines.

The knowledge-distilled alignment between parametric and standard RAG architectures marks a significant methodological advance in private reasoning with LLMs and information retrieval systems.