Unverified50% confidenceFactExact time
The study found differences between measurements at the decision level and the probability level, and auditing models should read model outputs as an application would, that is, focus on the top answer rather than merely the probability distribution.
1
Sources
50%
Confidence
Long-term
Relevance
10/30/2026
First Seen
Sources
Related Entities
Related Claims
UnverifiedThe expected cost of a decision depends not only on the probability that the model makes an incorrect judgment, but also on the actual consequences when an error occurs.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.71% similarUnverifiedThe confidence number a model reports about itself and a calibrated probability are not the same kind of object: the self-reported number comes from token sampling in the same forward pass as the answer, with no lookup of outcomes, whereas a calibrated probability must come from a paired record (join) of predictions and outcomes.71% similarUnverifiedIn recommendation scenarios, models typically use Root Mean Square Error (RMSE) to measure the error between predicted ratings and actual ratings.71% similarUnverifiedThe research team pointed out that the performance gains from inference-time context management in benchmarks exceeded most system-to-system differences that have been reported.70% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1115476API
curl https://kongchang.com/api/v1/knowledge/claims/1115476MCP
get_claim(id=1115476)