Unverified50% confidenceFactExact time
Confidence values typically originate from the model's softmax probability distribution over each option — a large probability gap yields a high confidence value, while a uniform distribution yields a low one.
1
Sources
50%
Confidence
Long-term
Relevance
10/7/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedConfidence is typically represented by the product of per-token probabilities or the sum of their logarithms.76% similarUnverifiedThe theoretical error rate of probabilistic data structures is built on the assumption of uniform hash distribution; if a hash function is biased toward a particular input distribution, the actual false positive rate will be significantly higher than the theoretical value.73% similarUnverifiedMost existing approaches rely on heuristic criteria to construct preference pairs, such as likelihood-based extremes that pick the samples the model assigns the highest and lowest probability as positive and negative examples.73% similarUnverifiedA model with slightly lower accuracy but five times cheaper may outperform on the valid findings per dollar metric, provided tiered routing is used to cover high-risk scenarios.70% similarUnverifiedDeep learning models are generally prone to overconfidence, where the probability values output by a model do not truly reflect the actual probability of its predictions being correct — a phenomenon known as poor calibration.69% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/985294API
curl https://kongchang.com/api/v1/knowledge/claims/985294MCP
get_claim(id=985294)