Out-of-Distribution
An important concept in machine learning referring to situations where the distribution of test data differs from that of training data. Models encounter out-of-distribution inputs when facing new users, new scenarios, or new languages.
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Agents that blindly trust their own memory are prone to making confident errors in out-of-distribution scenarios.
An out-of-distribution scenario refers to a situation where the input data encountered during model inference differs significantly in distribution from the training or historical experience data.
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Agents that blindly trust their own memory are prone to making confident errors in out-of-distribution scenarios.
50%UnverifiedAn out-of-distribution scenario refers to a situation where the input data encountered during model inference differs significantly in distribution from the training or historical experience data.
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