docs/REVIEW.md

a. LLM-Based Paraphrasing

• Multi-model approach: Llama-8B (same architecture) and Gemini (Flash/Pro) models for reliability
• Difficulty levels: Easy vs. Hard paraphrasing modes to effectively use different models with auditing.
• Medical context preservation: Maintains clinical terminology accuracy
• Configurable ratios: User-defined augmentation percentages

b. Back-Translation Augmentation

• Pivot languages EN-VI-EN-VI...
• Quality control: Length and semantic similarity validation
• Meaning preservation: Maintains semantic accuracy through translation cycles

c. Style Standardization

• Clinical voice enforcement: Neutral, professional medical tone
• Absolute language removal: Replaces guarantees with probabilistic language
• Forum sign-off removal: Eliminates informal communication patterns

d. Multi-Variant Generation (for reasoning)

• Answer variants: Concise, detailed, clinical, patient-friendly styles
• Question variants: Clarifying, follow-up, symptom-focused, treatment-focused
• Cross combinations: All question × answer variant combinations (up to 9 per sample) e. Clinical Scenario Creation
• Context variations: Emergency room, routine checkup, chronic conditions, family member perspectives
• Enhanced diversity: Multiple reasoning paths for improved model training

f. Quality Assurance f1. Data Cleaning

• PHI removal: Email, phone, URL, IP address redaction
• Deduplication: MD5-based content hashing with normalized comparison
• Invalid response handling: Detection and retry logic for failed responses
• Conversational element cleaning: Removal of greetings and non-medical content

f2. Validation

• Medical accuracy validation: LLM-based consistency checking
• Length control: Configurable maximum character limits
• Language detection: English validation for content quality

g. Output Formats: SFT Format

• Instruction: Task description
• Input: User question/context
• Output: Model response
• Metadata: Augmentation tags and source information