LLM Reliability and Robustness

Core Concepts

Model Reliability

• Consistency - Output stability across similar inputs
• Accuracy - Factual correctness and precision
• Robustness - Performance under varying conditions
• Determinism - Reproducibility of results

Common Challenges

• Hallucination - Generation of false information
• Bias - Systematic errors in model outputs
• Context Sensitivity - Varying performance with input context
• Edge Cases - Handling unusual or rare scenarios

Best Practices

Input Processing

• Prompt engineering guidelines
• Input validation techniques
• Context window management
• Error handling strategies

Output Validation

• Response verification methods
• Quality assurance checks
• Fact-checking mechanisms
• Consistency monitoring

Research and Resources

Academic Papers

• Hallucination in LLMs - Understanding and mitigating false outputs
• Measuring Reliability in LLMs

Industry Reports

• Google AI Reliability Report
• Microsoft Research on LLM Robustness
• Anthropic’s Constitutional AI

Tools and Frameworks

• ProtectAI - LLM security and reliability platform
• TruLens - Model evaluation framework
• DeepChecks - Testing and validation suite
• LangKit - LLM monitoring toolkit

Additional Resources

• Stanford CRFM Evaluation
• EleutherAI Model Evaluation