Top-k Sampling
Definition
Top-k sampling selects the next text token from a limited top group.
A decoding method that keeps only the k most probable next tokens, renormalizes, and randomly selects from them.
Simply explained
The AI takes the k most probable next text pieces into a selection box and draws one according to their probabilities.
Why it matters in practice
The term helps to select, develop, evaluate AI systems in a well-founded manner and realistically assess their technical risks.
Example from business
A text assistant tests fixed k values against factual accuracy, diversity, and formatting errors.
Limits and pitfalls
- A fixed k does not adapt to uncertainty; small values impoverish, large ones allow implausible tokens.
Origin & history
Top-k was popularized with neural language generation as a simple limit on the long tail of the distribution.
Use in mid-sized companies
- Possible example: A text assistant tests fixed k values against factual accuracy, diversity, and formatting errors.
Frequently asked questions
What does Top-k sampling mean?
A decoding method that keeps only the k most probable next tokens, renormalizes, and randomly selects from them. What should be considered? A fixed k does not adapt to uncertainty; small values impoverish, large ones allow implausible tokens.
Sources
- The Curious Case of Neural Text Degeneration · Holtzman et al., The Curious Case of Neural Text Degeneration
Source checked on 10.10.2026. Editorial application examples are hypothetical; no evidence of real customer results.
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