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Decision models are trained on fixed data distributions and may experience rapid performance degradation when the feature distribution of business data changes (distribution shift), whereas general-purpose large language models typically have stronger generalization ability to new concepts
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10/9/2026
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VerifiedWhen language models are repeatedly trained on data generated by themselves or similar models, systematic degradation occurs, with the diversity of model outputs decreasing with each generation and the tail information of the distribution gradually being lost.80% similarVerifiedData drift or concept drift refers to the phenomenon where model performance degrades significantly when the real-world data distribution changes while the model is still trained on old data; the industry addresses this with continuous learning and active learning strategies.79% similarUnverifiedA pragmatic strategy for handling distribution shift is to set up an uncertainty-domain expansion mechanism for the decision model: when the model's overall confidence drops, automatically increase the proportion routed to the LLM and trigger retraining78% similarVerifiedDistribution shift refers to the systematic difference between the statistical distribution of data seen during model training and the distribution of data in the actual deployment environment.77% similarVerifiedWhen input constraints are reduced, machine learning models rely more heavily on their prior distribution—that is, the default understanding of a concept formed during training.76% similar
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