DRAG introduces a novel distillation framework that transfers RAG capabilities from LLMs to SLMs through Evidence-based distillation and Graph-based structuring
Small Models, Big Knowledge: How DRAG Bridges the AI Efficiency-Accuracy Gap
👉 Why This Matters
Modern AI systems face a critical tension: large language models (LLMs) deliver impressive knowledge recall but demand massive computational resources, while smaller models (SLMs) struggle with factual accuracy and "hallucinations." Traditional retrieval-augmented generation (RAG) systems amplify this problem by requiring constant updates to vast knowledge bases.
👉 The Innovation
DRAG introduces a novel distillation framework that transfers RAG capabilities from LLMs to SLMs through two key mechanisms:
1. Evidence-based distillation: Filters and ranks factual snippets from teacher LLMs
2. Graph-based structuring: Converts retrieved knowledge into relational graphs to preserve critical connections
This dual approach reduces model size requirements by 10-100x while improving factual accuracy by up to 27.7% compared to prior methods like MiniRAG.
👉 How It Works
1. Evidence generation: A large teacher LLM produces multiple context-relevant facts
2. Semantic filtering: Combines cosine similarity and LLM scoring to retain top evidence
3. Knowledge graph creation: Extracts entity relationships to form structured context
4. Distilled inference: SLMs generate answers using both filtered text and graph data
The process mimics how humans combine raw information with conceptual understanding, enabling smaller models to "think" like their larger counterparts without the computational overhead.
👉 Privacy Bonus
DRAG adds a privacy layer by:
- Local query sanitization before cloud processing
- Returning only de-identified knowledge graphs
Tests show 95.7% reduction in potential personal data leakage while maintaining answer quality.
👉 Why It’s Significant
This work addresses three critical challenges simultaneously:
- Makes advanced RAG capabilities accessible on edge devices
- Reduces hallucination rates through structured knowledge grounding
- Preserves user privacy in cloud-based AI interactions
The GitHub repository provides full implementation details, enabling immediate application in domains like healthcare diagnostics, legal analysis, and educational tools where accuracy and efficiency are non-negotiable.
DRAG introduces a novel distillation framework that transfers RAG capabilities from LLMs to SLMs through two key mechanisms:1. Evidence-based distillation: Filters and ranks factual snippets from teacher LLMs2. Graph-based structuring: Converts retrieved knowledge into relational graphs to preserve critical connections
