Confidence
A quantitative indicator in statistics and machine learning expressing the subjective probability or degree of certainty a model assigns to a given outcome. It can be calibrated after the fact using metrics such as the Brier score, and is verifiable only when bound to a specific proposition.
Timeline (last 90 days)
The language model's confidence is a self-assessed score (0 to 1), not a calibrated probability; the model may give high confidence to wrong answers and may give low scores to correct answers, so it should be treated as evidence rather than permission.
In prediction markets, confidence usually denotes the subjective probability a model assigns to a binary or multinomial outcome and can be calibrated after the fact using metrics such as the Brier score; in LLM-driven systems it is often obtained from model self-assessment or by averaging the logits of multiple samples, with reliability far lower than that of statistical models.
The confidence reported by the model is severely disconnected from actual accuracy: the 95% confidence tier had an actual accuracy of 0%, while the 90% and 70% tiers both had an actual accuracy of 100%.
The model showed the highest confidence (95%) on underdetermined problems (problems with no unique correct answer), exhibiting a reverse calibration phenomenon.
Calibration measures whether the confidence reported by the model matches actual accuracy; an unvalidated confidence interface may mislead downstream decisions and should not be shipped.
The selection of the confidence threshold is essentially a Pareto trade-off between recall rate and cost
In AI systems, confidence is typically expressed as a probability value between 0 and 1, reflecting the model's estimate of the correctness of its own output.
Setting the confidence threshold is a risk-reward tradeoff: a threshold that is too high causes simple tickets to be routed back to humans, limiting efficiency gains, while a threshold that is too low causes error rates to climb on ambiguous cases; it is recommended to collect data via shadow mode and use ROC curve analysis to determine the threshold.
Confidence-based position selection strategies cannot themselves bypass the constraint of conditional independence; high confidence does not equate to a correct joint distribution.
Laya's output includes answers with confidence scores, where the confidence score reflects the degree of certainty the model has in its current judgment, allowing downstream systems to decide whether to trigger manual review or escalate to a more powerful model.
All Facts (10)
The language model's confidence is a self-assessed score (0 to 1), not a calibrated probability; the model may give high confidence to wrong answers and may give low scores to correct answers, so it should be treated as evidence rather than permission.
50%UnverifiedIn prediction markets, confidence usually denotes the subjective probability a model assigns to a binary or multinomial outcome and can be calibrated after the fact using metrics such as the Brier score; in LLM-driven systems it is often obtained from model self-assessment or by averaging the logits of multiple samples, with reliability far lower than that of statistical models.
50%UnverifiedThe confidence reported by the model is severely disconnected from actual accuracy: the 95% confidence tier had an actual accuracy of 0%, while the 90% and 70% tiers both had an actual accuracy of 100%.
50%UnverifiedThe model showed the highest confidence (95%) on underdetermined problems (problems with no unique correct answer), exhibiting a reverse calibration phenomenon.
50%UnverifiedCalibration measures whether the confidence reported by the model matches actual accuracy; an unvalidated confidence interface may mislead downstream decisions and should not be shipped.
50%UnverifiedThe selection of the confidence threshold is essentially a Pareto trade-off between recall rate and cost
50%UnverifiedIn AI systems, confidence is typically expressed as a probability value between 0 and 1, reflecting the model's estimate of the correctness of its own output.
50%UnverifiedSetting the confidence threshold is a risk-reward tradeoff: a threshold that is too high causes simple tickets to be routed back to humans, limiting efficiency gains, while a threshold that is too low causes error rates to climb on ambiguous cases; it is recommended to collect data via shadow mode and use ROC curve analysis to determine the threshold.
50%UnverifiedConfidence-based position selection strategies cannot themselves bypass the constraint of conditional independence; high confidence does not equate to a correct joint distribution.
50%UnverifiedLaya's output includes answers with confidence scores, where the confidence score reflects the degree of certainty the model has in its current judgment, allowing downstream systems to decide whether to trigger manual review or escalate to a more powerful model.
50%