DPF-CM: A Data Processing Framework with Privacy-Preserving Vector Databases for Chinese Medical LLMs Training and Deployment
Revolutionizing Chinese Medical LLM Development through Data-Centric AI
DPF-CM addresses critical gaps in Chinese medical LLM development by introducing a holistic data processing framework. It optimizes training data through novel instruction generation and preference data denoising, and enhances deployment privacy via a Privacy Preserving Vector Database (PPVD). Experiments show DPF-CM significantly boosts model accuracy, achieving SOTA performance among open-source counterparts, and reduces training data privacy leakage by 27%.
Quantifiable Impact of DPF-CM
Our framework delivers measurable improvements in both model performance and data security for enterprise-grade Chinese Medical LLMs.
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Model Accuracy Improvement (Win Rate)
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Privacy Leakage Reduction
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
DPF-CM introduces a comprehensive data processing pipeline for Chinese medical LLMs across Continued Pre-Training, Supervised Fine-Tuning (SFT), and Reinforcement Learning stages. This includes advanced cleaning, context-learning for instruction generation, and ensemble-based filtering for preference data, addressing the lack of structured instructions and noisy samples in existing datasets. The framework significantly enhances the model's ability to learn domain knowledge and generalize across tasks.
A key innovation is the Privacy Preserving Vector Database (PPVD) approach for deployment. PPVD identifies and stores 'high-risk' training data embeddings, then constructs corresponding 'secure' embeddings. During inference, user prompts are compared against the high-risk database; if a match is found, the response is generated from the secure database, minimizing privacy leakage from inadvertently exposed training data. This mechanism reduces privacy leakage by 27%.
DPF-CM leads to substantial improvements in model accuracy and overall performance. Our trained Chinese medical LLM achieves state-of-the-art results among open-source counterparts across single-turn, multi-turn dialogues, medical benchmarks, and terminology explanation tasks. Ablation studies confirm the effectiveness of each data optimization strategy, demonstrating the framework's robust contribution to model capabilities.
DPF-CM Overall Data Processing Flow
A visual representation of DPF-CM's holistic approach to data lifecycle management for Chinese medical LLMs.
Data Collection & Cleaning
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Pre-training Data Generation
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SFT Data Deduplication & Optimization
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Question-Oriented Instruction Generation
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Preference Data Generation & Denoising
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Model Memory Searches
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High-Risk DB Construction
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Secure DB Construction
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Match and Replace at Inference
Key Privacy Achievement
27% Reduction in Training Data Privacy LeakageDPF-CM's Privacy Preserving Vector Database (PPVD) method significantly reduces the risk of sensitive training data exposure during deployment, with experimental results showing a 27% decrease in average similarity of high-risk samples.
| Evaluation Metric | DPF-CM | Raw Data (Original) |
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| Medical Dialogue Win Rate (AI Eval) |
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| Medical Terminology Win Rate (AI Eval) |
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| Benchmark Accuracy (Multiple Choice) |
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| Human Evaluation (Win Rate) |
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Enhanced Medical Dialogue Capabilities
DPF-CM enables our trained Chinese medical LLM to achieve state-of-the-art performance in complex medical dialogue tasks, including both single-turn and multi-turn conversations. This improvement stems from structured instruction generation and rigorous data curation, allowing the LLM to better understand patient queries and provide professional, safe, and fluent responses, surpassing other open-source models.
Key Takeaway: The framework's data-centric approach directly translates to a significant leap in conversational AI for healthcare.
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Your Enterprise AI Implementation Roadmap
A phased approach to integrate DPF-CM into your existing infrastructure and achieve maximum impact.
Phase 1: Data Strategy & Ingestion
Comprehensive audit of existing medical datasets, identification of proprietary data sources, and implementation of DPF-CM's data collection and cleaning pipelines. Establish secure data ingestion pathways compliant with healthcare regulations.
Phase 2: Model Training & Fine-tuning
Leverage DPF-CM's advanced pre-training, SFT, and RL pipelines using your curated data. Focus on question-oriented instruction generation and preference data denoising to build a robust, domain-specific Chinese medical LLM.
Phase 3: Privacy Preservation & Deployment
Integrate the Privacy Preserving Vector Database (PPVD) for secure model deployment. Establish protocols for high-risk data identification, secure database construction, and match-and-replace inference to protect patient privacy.
Phase 4: Monitoring, Optimization & Scaling
Implement continuous monitoring for model performance and privacy compliance. Regularly update and optimize training data and models. Scale the DPF-CM framework and LLM deployment across various enterprise applications within your organization.
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