Unverified50% confidenceFactExact time
A large language model's confidence signal can be extracted from the output probability distribution, for example by averaging the logprobs of candidate tokens or computing entropy.
1
Sources
50%
Confidence
Long-term
Relevance
10/18/2026
First Seen
Sources
Related Entities
Related Claims
VerifiedLarge language models can approximately estimate decision confidence by analyzing token probability distributions, explicitly outputting self-evaluation scores, or observing answer consistency across multiple samplings.76% similarUnverifiedLarge language models are probability-based text prediction systems; vague inputs cause the model to sample in a high-entropy state, and outputs tend toward the most common generic answers found in training data75% similarUnverifiedAn autoregressive language model produces a logit vector of vocabulary size at each prediction step, and its structure determines the conditional probability distribution of the next token.74% similarUnverifiedLLM可通过分析输出token的对数概率分布、思维链末尾自我评估、温度采样统计答案分布熵值等手段近似估计决策置信度71% similarUnverifiedThe confidence scores output by language models are softmax-scaled probabilities reflecting relative preference rather than calibrated true probabilities, and models that have undergone RLHF or instruction fine-tuning are generally poorly calibrated.71% similar
Cite This Claim
Stable URI
https://kongchang.com/claim/1059994API
curl https://kongchang.com/api/v1/knowledge/claims/1059994MCP
get_claim(id=1059994)