Sampling Techniques

Methods for selecting tokens during AI text generation based on probability distributions

Overview

Sampling techniques are methods used to control how AI models select their next tokens during text generation. These approaches balance between deterministic and creative outputs by manipulating the probability distributions of potential tokens.

Core Methods

Key sampling approaches include:

  • Top-k sampling
    • Limits selection to k most likely tokens
    • Controls output randomness
    • Maintains coherence
  • Nucleus (top-p) sampling
    • Selects from top probability mass
    • Adapts to context dynamically
    • Balances diversity and quality

Implementation Strategies

Effective sampling requires:

  • Temperature adjustment
  • Probability thresholding
  • Distribution manipulation
  • Context awareness
  • Output validation
  • Quality control

Common Applications

Used primarily in:

  • Text generation
  • Dialogue systems
  • Creative writing
  • Content creation
  • Language translation
  • Code generation