Your team has done a truly outstanding job. RAGChecker was extremely helpful and allowed us to quickly analyze multiple metrics for RAG, thank you very much! I hope RAGChecker can integrate with LLM to provide specific scores (out of 100) and provide specific optimization recommendations to achieve higher scores and provide excellent RAG services.
example
Overall Metrics: 30%
Precision (10%)
Recall (10%)
F1 (10%)
Retriever Metrics: 35%
Claim Recall (20%)
Context Precision (15%)
Generator Metrics: 35%
Context Utilization (15%)
Noise Sensitivity (Relevant) (5%)
Noise Sensitivity (Irrelevant) (5%)
Hallucination (5%)
Self Knowledge (5%)
Faithfulness (5%)
Retriever Optimization
Issue: Low Context Precision (50%) - Retrieved results contain excessive irrelevant context.
Suggestions:
Optimize the retrieval model (e.g., adjust similarity thresholds or introduce re-ranking techniques).
Add diversity filtering for retrieved results (e.g., deduplication or clustering).
Expected Improvement: Context Precision → 70%
Score Increase: (70 - 50) × 0.15 = **+3.0** → Total Score **+3.0**
Generator Optimization
Issue 1: Low Context Utilization (47.1%) - Insufficient use of valid context.
Suggestions:
Introduce attention mechanisms to strengthen context-query alignment.
Train the generator to prioritize extracting critical information.
Expected Improvement: Context Utilization → 65%
Score Increase: (65 - 47.1) × 0.15 = **+2.69** → Total Score **+2.69**
Issue 2: High Noise Sensitivity (Relevant) (22.2%) - Noise in relevant passages degrades output quality.
Suggestions:
Enhance generator robustness against noise (e.g., adversarial training).
Add a noise-filtering module to preprocess context.
Expected Improvement: Noise Sensitivity (Relevant) → 10%
Score Increase: (22.2 - 10) × 0.05 = **+0.61** → Total Score **+0.61**
Issue 3: Extremely Low Self Knowledge (3.7%) - Poor ability to leverage internal knowledge.
Suggestions:
Allow the generator to access pre-trained knowledge bases in low-retrieval-quality scenarios.
Implement hybrid strategies (e.g., blending retrieved and pre-trained knowledge).
Expected Improvement: Self Knowledge → 15%
Score Increase: (15 - 3.7) × 0.05 = **+0.57** → Total Score **+0.57**
Total Expected Score
Optimized Total: 74.11 + 3.0 + 2.69 + 0.61 + 0.57 ≈ 80.98 → 81 points (▲ ~7 points).
Final Conclusion
Current Score: 74 points (below average; both retrieval and generation need improvement).
Optimization Focus: Prioritize context precision, context utilization, and noise robustness.
Potential Upper Limit: With comprehensive optimizations (e.g., improving Faithfulness and reducing Hallucination), total score could reach 85+ points.
Note: Score weights align with RAGChecker's evaluation framework (e.g., Context Precision contributes 15% to the total score). Metrics like "Self Knowledge" and "Noise Sensitivity" follow definitions from RAGAs and TruLens.
Your team has done a truly outstanding job. RAGChecker was extremely helpful and allowed us to quickly analyze multiple metrics for RAG, thank you very much! I hope RAGChecker can integrate with LLM to provide specific scores (out of 100) and provide specific optimization recommendations to achieve higher scores and provide excellent RAG services.
example
Overall Metrics: 30%
Precision (10%)
Recall (10%)
F1 (10%)
Retriever Metrics: 35%
Claim Recall (20%)
Context Precision (15%)
Generator Metrics: 35%
Context Utilization (15%)
Noise Sensitivity (Relevant) (5%)
Noise Sensitivity (Irrelevant) (5%)
Hallucination (5%)
Self Knowledge (5%)
Faithfulness (5%)
Retriever Optimization
Issue: Low Context Precision (50%) - Retrieved results contain excessive irrelevant context.
Suggestions:
Optimize the retrieval model (e.g., adjust similarity thresholds or introduce re-ranking techniques).
Add diversity filtering for retrieved results (e.g., deduplication or clustering).
Expected Improvement: Context Precision → 70%
Score Increase: (70 - 50) × 0.15 = **+3.0** → Total Score **+3.0**
Generator Optimization
Issue 1: Low Context Utilization (47.1%) - Insufficient use of valid context.
Suggestions:
Introduce attention mechanisms to strengthen context-query alignment.
Train the generator to prioritize extracting critical information.
Expected Improvement: Context Utilization → 65%
Score Increase: (65 - 47.1) × 0.15 = **+2.69** → Total Score **+2.69**
Issue 2: High Noise Sensitivity (Relevant) (22.2%) - Noise in relevant passages degrades output quality.
Suggestions:
Enhance generator robustness against noise (e.g., adversarial training).
Add a noise-filtering module to preprocess context.
Expected Improvement: Noise Sensitivity (Relevant) → 10%
Score Increase: (22.2 - 10) × 0.05 = **+0.61** → Total Score **+0.61**
Issue 3: Extremely Low Self Knowledge (3.7%) - Poor ability to leverage internal knowledge.
Suggestions:
Allow the generator to access pre-trained knowledge bases in low-retrieval-quality scenarios.
Implement hybrid strategies (e.g., blending retrieved and pre-trained knowledge).
Expected Improvement: Self Knowledge → 15%
Score Increase: (15 - 3.7) × 0.05 = **+0.57** → Total Score **+0.57**
Total Expected Score
Optimized Total: 74.11 + 3.0 + 2.69 + 0.61 + 0.57 ≈ 80.98 → 81 points (▲ ~7 points).
Final Conclusion
Current Score: 74 points (below average; both retrieval and generation need improvement).
Optimization Focus: Prioritize context precision, context utilization, and noise robustness.
Potential Upper Limit: With comprehensive optimizations (e.g., improving Faithfulness and reducing Hallucination), total score could reach 85+ points.
Note: Score weights align with RAGChecker's evaluation framework (e.g., Context Precision contributes 15% to the total score). Metrics like "Self Knowledge" and "Noise Sensitivity" follow definitions from RAGAs and TruLens.