Unverified50% confidenceFactExact time
Common sampling strategies include top-k sampling (retaining only the k tokens with the highest probability), top-p sampling (dynamically selecting the minimum set of tokens whose cumulative probability just exceeds a threshold p), and the temperature parameter (controlling the sharpness of the distribution by scaling the logits).
1
Sources
50%
Confidence
Long-term
Relevance
9/5/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedTop-p（核采样）通过限定候选token的累积概率范围来控制输出多样性，与Temperature配合使用可实现更精细的输出调节80% similarUnverifiedTemperature controls the flatness of the sampling distribution; a higher value tends toward selecting lower-probability words, while Top-p dynamically narrows the candidate vocabulary set through a cumulative probability threshold.77% similarVerifiedThe non-determinism of LLMs stems from sampling strategies; the Temperature parameter controls the randomness of the output distribution, while Top-p and Top-k further constrain the range of candidate tokens.76% similarUnverifiedTop-p (nucleus sampling) limits the candidate token range by dynamically truncating probability mass, and has better adaptability than fixed-truncation Top-k71% similarUnverifiedCommon strategies for dealing with long-tail distributions include oversampling (e.g., SMOTE), undersampling, class weight adjustment, few-shot learning, and using large model zero-shot capabilities as a fallback.66% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/857781API
curl https://kongchang.com/api/v1/knowledge/claims/857781MCP
get_claim(id=857781)