Prompt LLM Improvement Method
PLIM
Below higlights the implementation of the PromptEval method for a clinical environment using novel methods to decrease the risk of prompt and llm tools, and increase the speed of their safe development.
PLIM incorporates clinical pathways, best practice, clinical synthetic data and continous monitoring via a system that can be integrated into LLM based systems design, developent and deploymemt.
PLIM incorporates clinical pathways, best practice, clinical synthetic data and continous monitoring via a system that can be integrated into LLM based systems design, developent and deploymemt.
The Use Case for PLIM
Example Inputs
Approach
Validation Data in Particular Use Cases
The importance of valid data sources and clinical input in the deployment of PLIM is transparent to CarefulAI. PLIM and this theme are covered in the Critical AI, on AI podcast
User Interface in Mental Health
System Diagram
See the following links for
Examples of LLM evaluation Next Steps
For passive medical summary applications
Clinical Perplexity Calculations
Python Methods
Examples of Monitoring System Inputs
User input monitoring
LLM input monitoring
Examples of LLM evaluation Next Steps
For passive medical summary applications
Clinical Perplexity Calculations
Python Methods
Examples of Monitoring System Inputs
User input monitoring
LLM input monitoring
Paper
PLIM: A Prompt Language Model Improvement Method for Healthcare Safety
Authors: Bourne, J. White, W. Connor, J
Institution: CarefulAI
## Abstract
This paper introduces PLIM (Prompt Language Model Improvement Method), a novel framework for evaluating and improving prompt safety in healthcare applications of Large Language Models (LLMs). Given the critical dependency between prompts and LLM outputs in healthcare contexts, traditional LLM benchmarking alone is insufficient. PLIM provides a comprehensive approach combining Item Response Theory (IRT), balanced sampling, and real-time safety monitoring to ensure reliable and safe prompt-LLM interactions in clinical settings.
## 1. Introduction
The deployment of Large Language Models (LLMs) in healthcare settings presents unique challenges due to the critical nature of medical decision-making and patient safety. While significant attention has been paid to model performance metrics, the relationship between prompt design and LLM responses remains inadequately addressed. This paper introduces PLIM, a methodological framework that specifically addresses the co-dependency between prompts and LLM outputs in healthcare applications.
### 1.1 Motivation
Traditional LLM benchmarking approaches focus primarily on model performance metrics, overlooking the critical role of prompt design in output reliability. In healthcare applications, where incorrect outputs could have serious consequences, this oversight is particularly problematic. PLIM addresses this gap by providing a systematic approach to evaluating and improving prompt-LLM interactions.
### 1.2 Contributions
This paper makes the following contributions:
1. A systematic framework for evaluating prompt-LLM interactions in healthcare
2. An IRT-based statistical foundation for prompt analysis
3. Real-time safety monitoring and validation systems
4. Healthcare-specific enhancement features building upon basic prompt evaluation
## 2. System Architecture
### 2.1 Foundation Layer
PLIM builds upon three core components:
1. IRT Model Statistical Base: Provides theoretical framework for analyzing prompt-response patterns
2. Balanced Sampling: Ensures comprehensive coverage of clinical scenarios
3. Distribution Estimation: Enables robust statistical analysis of LLM responses
### 2.2 Healthcare Sources Integration
The system integrates multiple healthcare-specific data sources:
- Clinical Guidelines: Define structure and inform prompt templates
- Assessment Tools: Provide scenarios and define metrics
- Expert Input: Reviews and validates implementations
- Synthetic Data: Generates test cases
- Safety Protocols: Constrain and validate responses
### 2.3 Core Processing Components
PLIM implements several key processing modules:
1. Real-time Processing:
- Event Queue Management
- WebSocket Server for immediate updates
2. Data Management:
- Primary Database
- Analytics Database
- Cache Layer
3. Monitoring and Analytics:
- LLM Evaluator
- Alert System
- Analytics Engine
## 3. Statistical Framework
### 3.1 Data Collection and Sampling
The system employs a sophisticated data collection process:
1. Response Data (Y_ij): Captures LLM outputs
2. Covariates (x_i, z_j): Records contextual variables
3. Two-way Balanced Sampling: Creates representative evaluation sets
### 3.1.1 Empirical Performance Data
Recent testing demonstrates PLIM can accurately estimate performance quantiles across 100 prompt templates with minimal evaluation budget. Key findings include:
- Accurate estimation achieved with only 200-1600 total evaluations
- Represents just 0.81% to 1.15% of total possible evaluations
- Demonstrated robustness across MMLU, BIG-bench Hard (BBH), and LMentry benchmarks
### 3.2 Core Calculations
PLIM utilizes an advanced statistical framework:
1. IRT Model Parameters (ψ,γ):
- G(θ_i - β_j) function for response probability
- X-pIRT Estimator for performance evaluation
2. Distribution Analysis:
- Quantile calculations
- Performance spread metrics
### 3.3 Sensitivity Metrics
Four key metrics are implemented:
1. Performance Spread: max(S_i) - min(S_i)
2. Variance: Var(S_i)
3. Stability Score: 1/CV(S_i)
4. Risk Score: P(S_i < threshold)
### 3.4 Monitoring Statistics
Continuous monitoring includes:
1. Drift Detection: KL(F_t || F_{t-1})
2. Alert Triggers: I(metric > threshold)
3. Trend Analysis: Δmetrics/Δt
## 4. Healthcare-Specific Enhancements
### 4.1 Enhanced Features
PLIM extends basic prompt evaluation with:
1. Real-time Processing: Immediate response validation
2. Clinical Validation: Domain-specific safety checks
3. Safety Monitoring: Continuous risk assessment
4. Audit System: Comprehensive tracking
### 4.2 Enhanced Outputs
The system provides healthcare-specific outputs:
1. Clinical Metrics: Domain-relevant performance measures
2. Safety Reports: Comprehensive risk assessments
3. Audit Trails: Complete interaction histories
## 5. Implementation and Testing
### 5.1 Testing Framework
PLIM implements a robust testing architecture:
1. Mock Data Generation
2. Test Scenarios
3. Multiple Testing Levels:
- Load Tests
- Unit Tests
- Integration Tests
### 5.2 Output Interfaces
Multiple interfaces ensure accessibility:
1. Dashboard UI: Real-time monitoring
2. API Endpoints: Programmatic access
3. Alert Notifications: Immediate risk communication
4. Export System: Data extraction and analysis
## 6. Conclusion
PLIM represents a significant advance in ensuring safe and reliable LLM deployment in healthcare settings. By addressing the critical co-dependency between prompts and LLM outputs, PLIM provides a comprehensive framework for evaluation, monitoring, and improvement of prompt-LLM interactions in clinical applications.
### 6.1 Future Work
Future developments may include:
1. Extended clinical validation frameworks
2. Enhanced real-time monitoring capabilities
3. Integration with additional healthcare systems
4. Expanded statistical modeling approaches
## 7. Analysis of LLM Prompt Sensitivity in Healthcare Contexts
### 7.1 Performance Spread Analysis
Testing across multiple LLMs revealed critical variations in prompt sensitivity that have direct implications for healthcare deployments:
Key Findings:
1. Overall Performance Stability:
- Aggregate performance showed relative stability
- Individual subject scores demonstrated significant inconsistency
- Critical healthcare tasks showed heightened sensitivity
2. Model-Specific Sensitivity:
- Average spread of 10% at subject level
- Highest variability in complex medical reasoning tasks
- Safety-critical tasks showed increased prompt sensitivity
### 7.2 Template Consistency Analysis
Analysis of template performance in healthcare contexts revealed:
1. Within-Model Consistency:
- Gemma-7B-it achieved highest consistency (Kendall's W: 0.45)
- Most models showed limited internal consistency
- Healthcare prompt templates required additional validation
2. Cross-Model Evaluation:
- No universal "best" template identified
- Maximum Kendall's W of 0.25 across subjects
- Healthcare applications require model-specific template optimization
### 7.3 Healthcare Safety Implications
1. Critical Safety Considerations:
- Template sensitivity directly impacts diagnostic accuracy
- Safety-critical tasks require redundant template validation
- Real-time monitoring of template performance essential
2. Risk Mitigation Strategies:
- Implement multi-template validation protocols
- Establish minimum performance thresholds across templates
- Regular recalibration of template performance metrics
3. Operational Guidelines:
- Maintain validated template repositories
- Implement template version control
- Regular performance audits across template variations
### 7.4 PLIM Healthcare Applications
Based on sensitivity analysis findings, PLIM implementation recommendations:
1. Template Management:
- Establish healthcare-specific template libraries
- Regular validation across multiple medical domains
- Continuous monitoring of template performance
2. Safety Protocols:
- Multi-template verification for critical decisions
- Automated template sensitivity monitoring
- Regular recalibration of safety thresholds
3. Performance Monitoring:
- Real-time template sensitivity tracking
- Automated alert systems for performance degradation
- Regular audit of template effectiveness
.
Authors: Bourne, J. White, W. Connor, J
Institution: CarefulAI
## Abstract
This paper introduces PLIM (Prompt Language Model Improvement Method), a novel framework for evaluating and improving prompt safety in healthcare applications of Large Language Models (LLMs). Given the critical dependency between prompts and LLM outputs in healthcare contexts, traditional LLM benchmarking alone is insufficient. PLIM provides a comprehensive approach combining Item Response Theory (IRT), balanced sampling, and real-time safety monitoring to ensure reliable and safe prompt-LLM interactions in clinical settings.
## 1. Introduction
The deployment of Large Language Models (LLMs) in healthcare settings presents unique challenges due to the critical nature of medical decision-making and patient safety. While significant attention has been paid to model performance metrics, the relationship between prompt design and LLM responses remains inadequately addressed. This paper introduces PLIM, a methodological framework that specifically addresses the co-dependency between prompts and LLM outputs in healthcare applications.
### 1.1 Motivation
Traditional LLM benchmarking approaches focus primarily on model performance metrics, overlooking the critical role of prompt design in output reliability. In healthcare applications, where incorrect outputs could have serious consequences, this oversight is particularly problematic. PLIM addresses this gap by providing a systematic approach to evaluating and improving prompt-LLM interactions.
### 1.2 Contributions
This paper makes the following contributions:
1. A systematic framework for evaluating prompt-LLM interactions in healthcare
2. An IRT-based statistical foundation for prompt analysis
3. Real-time safety monitoring and validation systems
4. Healthcare-specific enhancement features building upon basic prompt evaluation
## 2. System Architecture
### 2.1 Foundation Layer
PLIM builds upon three core components:
1. IRT Model Statistical Base: Provides theoretical framework for analyzing prompt-response patterns
2. Balanced Sampling: Ensures comprehensive coverage of clinical scenarios
3. Distribution Estimation: Enables robust statistical analysis of LLM responses
### 2.2 Healthcare Sources Integration
The system integrates multiple healthcare-specific data sources:
- Clinical Guidelines: Define structure and inform prompt templates
- Assessment Tools: Provide scenarios and define metrics
- Expert Input: Reviews and validates implementations
- Synthetic Data: Generates test cases
- Safety Protocols: Constrain and validate responses
### 2.3 Core Processing Components
PLIM implements several key processing modules:
1. Real-time Processing:
- Event Queue Management
- WebSocket Server for immediate updates
2. Data Management:
- Primary Database
- Analytics Database
- Cache Layer
3. Monitoring and Analytics:
- LLM Evaluator
- Alert System
- Analytics Engine
## 3. Statistical Framework
### 3.1 Data Collection and Sampling
The system employs a sophisticated data collection process:
1. Response Data (Y_ij): Captures LLM outputs
2. Covariates (x_i, z_j): Records contextual variables
3. Two-way Balanced Sampling: Creates representative evaluation sets
### 3.1.1 Empirical Performance Data
Recent testing demonstrates PLIM can accurately estimate performance quantiles across 100 prompt templates with minimal evaluation budget. Key findings include:
- Accurate estimation achieved with only 200-1600 total evaluations
- Represents just 0.81% to 1.15% of total possible evaluations
- Demonstrated robustness across MMLU, BIG-bench Hard (BBH), and LMentry benchmarks
### 3.2 Core Calculations
PLIM utilizes an advanced statistical framework:
1. IRT Model Parameters (ψ,γ):
- G(θ_i - β_j) function for response probability
- X-pIRT Estimator for performance evaluation
2. Distribution Analysis:
- Quantile calculations
- Performance spread metrics
### 3.3 Sensitivity Metrics
Four key metrics are implemented:
1. Performance Spread: max(S_i) - min(S_i)
2. Variance: Var(S_i)
3. Stability Score: 1/CV(S_i)
4. Risk Score: P(S_i < threshold)
### 3.4 Monitoring Statistics
Continuous monitoring includes:
1. Drift Detection: KL(F_t || F_{t-1})
2. Alert Triggers: I(metric > threshold)
3. Trend Analysis: Δmetrics/Δt
## 4. Healthcare-Specific Enhancements
### 4.1 Enhanced Features
PLIM extends basic prompt evaluation with:
1. Real-time Processing: Immediate response validation
2. Clinical Validation: Domain-specific safety checks
3. Safety Monitoring: Continuous risk assessment
4. Audit System: Comprehensive tracking
### 4.2 Enhanced Outputs
The system provides healthcare-specific outputs:
1. Clinical Metrics: Domain-relevant performance measures
2. Safety Reports: Comprehensive risk assessments
3. Audit Trails: Complete interaction histories
## 5. Implementation and Testing
### 5.1 Testing Framework
PLIM implements a robust testing architecture:
1. Mock Data Generation
2. Test Scenarios
3. Multiple Testing Levels:
- Load Tests
- Unit Tests
- Integration Tests
### 5.2 Output Interfaces
Multiple interfaces ensure accessibility:
1. Dashboard UI: Real-time monitoring
2. API Endpoints: Programmatic access
3. Alert Notifications: Immediate risk communication
4. Export System: Data extraction and analysis
## 6. Conclusion
PLIM represents a significant advance in ensuring safe and reliable LLM deployment in healthcare settings. By addressing the critical co-dependency between prompts and LLM outputs, PLIM provides a comprehensive framework for evaluation, monitoring, and improvement of prompt-LLM interactions in clinical applications.
### 6.1 Future Work
Future developments may include:
1. Extended clinical validation frameworks
2. Enhanced real-time monitoring capabilities
3. Integration with additional healthcare systems
4. Expanded statistical modeling approaches
## 7. Analysis of LLM Prompt Sensitivity in Healthcare Contexts
### 7.1 Performance Spread Analysis
Testing across multiple LLMs revealed critical variations in prompt sensitivity that have direct implications for healthcare deployments:
Key Findings:
1. Overall Performance Stability:
- Aggregate performance showed relative stability
- Individual subject scores demonstrated significant inconsistency
- Critical healthcare tasks showed heightened sensitivity
2. Model-Specific Sensitivity:
- Average spread of 10% at subject level
- Highest variability in complex medical reasoning tasks
- Safety-critical tasks showed increased prompt sensitivity
### 7.2 Template Consistency Analysis
Analysis of template performance in healthcare contexts revealed:
1. Within-Model Consistency:
- Gemma-7B-it achieved highest consistency (Kendall's W: 0.45)
- Most models showed limited internal consistency
- Healthcare prompt templates required additional validation
2. Cross-Model Evaluation:
- No universal "best" template identified
- Maximum Kendall's W of 0.25 across subjects
- Healthcare applications require model-specific template optimization
### 7.3 Healthcare Safety Implications
1. Critical Safety Considerations:
- Template sensitivity directly impacts diagnostic accuracy
- Safety-critical tasks require redundant template validation
- Real-time monitoring of template performance essential
2. Risk Mitigation Strategies:
- Implement multi-template validation protocols
- Establish minimum performance thresholds across templates
- Regular recalibration of template performance metrics
3. Operational Guidelines:
- Maintain validated template repositories
- Implement template version control
- Regular performance audits across template variations
### 7.4 PLIM Healthcare Applications
Based on sensitivity analysis findings, PLIM implementation recommendations:
1. Template Management:
- Establish healthcare-specific template libraries
- Regular validation across multiple medical domains
- Continuous monitoring of template performance
2. Safety Protocols:
- Multi-template verification for critical decisions
- Automated template sensitivity monitoring
- Regular recalibration of safety thresholds
3. Performance Monitoring:
- Real-time template sensitivity tracking
- Automated alert systems for performance degradation
- Regular audit of template effectiveness
.
Further Reading
1. Polo, F. M., Xu, R., Weber, L., Silva, M., Bhardwaj, O., Choshen, L., de Oliveira, A. F. M., Sun, Y., & Yurochkin, M. (2024). Efficient multi-prompt evaluation of LLMs. arXiv:2405.17202v2
2. Cai, L., Choi, K., Hansen, M., & Harrell, L. (2016). Item response theory. Annual Review of Statistics and Its Application, 3, 297-321.
3. Van der Linden, W. J. (2018). Handbook of item response theory: Three volume set. CRC Press.
4. Brzezińska, J. (2020). Item response theory models in the measurement theory. Communications in Statistics-Simulation and Computation, 49(12), 3299-3313.
5. Lord, F. M., Novick, M. R., & Birnbaum, A. (1968). Statistical theories of mental test scores.
6. Georg, R. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Institute of Education Research.
7. Chen, Y., Li, C., Ouyang, J., & Xu, G. (2023). Statistical inference for noisy incomplete binary matrix. Journal of Machine Learning Research, 24(95), 1-66.
8. Starke, A., Willemsen, M., & Snijders, C. (2017). Effective user interface designs to increase energy-efficient behavior in a Rasch-based energy recommender system. Proceedings of the eleventh ACM conference on recommender systems, 65-73.
9. Clements, D. H., Sarama, J. H., & Liu, X. H. (2008). Development of a measure of early mathematics achievement using the Rasch model: The research-based early maths assessment. Educational Psychology, 28(4), 457-482.
10. Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2023). Quantifying language models' sensitivity to spurious features in prompt design. arXiv preprint arXiv:2310.11324.
11. Mizrahi, M., Kaplan, G., Malkin, D., Dror, R., Shahaf, D., & Stanovsky, G. (2023). State of what art? a call for multi-prompt llm evaluation. arXiv preprint arXiv:2401.00595.
12. Weber, L., Bruni, E., & Hupkes, D. (2023). Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning. Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), 294-313.
13. Perlitz, Y., Bandel, E., Gera, A., Arviv, O., Ein-Dor, L., Shnarch, E., Slonim, N., Shmueli-Scheuer, M., & Choshen, L. (2023). Efficient benchmarking (of language models). arXiv preprint arXiv:2308.11696.
14. Nitsure, A., Mroueh, Y., Rigotti, M., Greenewald, K., Belgodere, B., Yurochkin, M., Navratil, J., Melnyk, I., & Ross, J. (2023). Risk assessment and statistical significance in the age of foundation models. arXiv preprint arXiv:2310.07132
1. Polo, F. M., Xu, R., Weber, L., Silva, M., Bhardwaj, O., Choshen, L., de Oliveira, A. F. M., Sun, Y., & Yurochkin, M. (2024). Efficient multi-prompt evaluation of LLMs. arXiv:2405.17202v2
2. Cai, L., Choi, K., Hansen, M., & Harrell, L. (2016). Item response theory. Annual Review of Statistics and Its Application, 3, 297-321.
3. Van der Linden, W. J. (2018). Handbook of item response theory: Three volume set. CRC Press.
4. Brzezińska, J. (2020). Item response theory models in the measurement theory. Communications in Statistics-Simulation and Computation, 49(12), 3299-3313.
5. Lord, F. M., Novick, M. R., & Birnbaum, A. (1968). Statistical theories of mental test scores.
6. Georg, R. (1960). Probabilistic models for some intelligence and attainment tests. Copenhagen: Institute of Education Research.
7. Chen, Y., Li, C., Ouyang, J., & Xu, G. (2023). Statistical inference for noisy incomplete binary matrix. Journal of Machine Learning Research, 24(95), 1-66.
8. Starke, A., Willemsen, M., & Snijders, C. (2017). Effective user interface designs to increase energy-efficient behavior in a Rasch-based energy recommender system. Proceedings of the eleventh ACM conference on recommender systems, 65-73.
9. Clements, D. H., Sarama, J. H., & Liu, X. H. (2008). Development of a measure of early mathematics achievement using the Rasch model: The research-based early maths assessment. Educational Psychology, 28(4), 457-482.
10. Sclar, M., Choi, Y., Tsvetkov, Y., & Suhr, A. (2023). Quantifying language models' sensitivity to spurious features in prompt design. arXiv preprint arXiv:2310.11324.
11. Mizrahi, M., Kaplan, G., Malkin, D., Dror, R., Shahaf, D., & Stanovsky, G. (2023). State of what art? a call for multi-prompt llm evaluation. arXiv preprint arXiv:2401.00595.
12. Weber, L., Bruni, E., & Hupkes, D. (2023). Mind the instructions: a holistic evaluation of consistency and interactions in prompt-based learning. Proceedings of the 27th Conference on Computational Natural Language Learning (CoNLL), 294-313.
13. Perlitz, Y., Bandel, E., Gera, A., Arviv, O., Ein-Dor, L., Shnarch, E., Slonim, N., Shmueli-Scheuer, M., & Choshen, L. (2023). Efficient benchmarking (of language models). arXiv preprint arXiv:2308.11696.
14. Nitsure, A., Mroueh, Y., Rigotti, M., Greenewald, K., Belgodere, B., Yurochkin, M., Navratil, J., Melnyk, I., & Ross, J. (2023). Risk assessment and statistical significance in the age of foundation models. arXiv preprint arXiv:2310.07132
