Unverified50% confidenceTradeoffExact time
Likelihood-based UQ methods directly read the log probabilities of output tokens from the model, but require access to internal model parameters, making them infeasible for closed-source models such as GPT-4.
1
Sources
50%
Confidence
Long-term
Relevance
10/4/2026
First Seen
Sources
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Related Claims
VerifiedEach inference requires loading model weights into GPU memory and generating responses token by token; GPT-4-level model parameter counts are typically on the order of hundreds of billions.75% similarUnverified开放权重模型（如Meta的LLaMA系列、Mistral）公开模型参数权重供任何人下载使用，与GPT-4、Claude等闭源API模型形成对比69% similarUnverifiedOffline translation quality is generally slightly inferior to cloud-based models at the GPT-4 level, because on-device models are physically constrained by parameter count and compute.68% similarVerifiedThe GPT series of models uses an autoregressive generation approach, predicting the next most likely output token by token.68% similarUnverifiedOn convergent tasks such as code debugging, SQL generation, and documentation writing, the gap between GPT-4-level models and their successors is relatively limited.67% similar
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