Category:
Category:
Adaptive Sampling
Category:
Model Inference & Optimization
Definition
Dynamically adjusting sampling parameters to improve accuracy and reduce hallucinations.
Explanation
Adaptive sampling modifies generation parameters like temperature, top-k, and top-p during inference based on model confidence, task type, or detected uncertainty. This ensures stable, deterministic outputs for critical tasks and more creative outputs where needed. Enterprises use adaptive sampling to reduce hallucinations in high-risk workflows.
Technical Architecture
LLM Output Probabilities → Confidence Analyzer → Adaptive Sampler → Final Generation
Core Component
Confidence scoring, sampling controller, task classifier
Use Cases
Enterprise copilots, analytics, summarization, compliance-heavy outputs
Pitfalls
Misconfigured sampling reduces creativity or stability
LLM Keywords
Related Concepts
Related Frameworks
• Confidence Scoring
• Hallucination Mitigation
• Model Routing