Unverified50% confidenceFactExact time
Underfitting means the model is too simple to learn even the basic patterns of the training data, with both training error and test error high.
1
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50%
Confidence
Long-term
Relevance
10/25/2026
First Seen
Sources
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Related Claims
UnverifiedDeepMind's Chinchilla research pointed out that previous models were generally oversized and undertrained, meaning they had too many parameters but insufficient training data.77% similarUnverifiedOverfitting refers to the phenomenon where a model performs extremely well on the training set but generalizes poorly to new data, and this risk is especially prominent in pharmacy datasets with relatively small sample sizes.75% similarUnverifiedAs the scale of pretrained models increases, many previously effective explicit knowledge injection techniques have gradually become ineffective or even produced negative effects.74% similarVerifiedThe quality and diversity of training data directly affects the reliability of model outputs, and training data containing large amounts of low-quality, contradictory, or false information can cause models to learn incorrect patterns.74% similar
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