Unverified50% confidenceFactExact time
Uneven distribution of training corpus is the root cause of deviations in model capability, and a large model's code generation ability for a language depends heavily on the volume, diversity and quality of that language's code in the pre-training data.
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50%
Confidence
Long-term
Relevance
10/19/2026
First Seen
Sources
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Related Claims
VerifiedThe underlying mechanism by which large language models generate code is conditional probability prediction based on massive training corpora, and the model does not truly understand code logic79% similarVerifiedWhen 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.78% similarVerifiedIn-context learning was first observed in large language models, and its capability fundamentally stems from the diversity of pretraining data.78% similarVerifiedWhen input information is incomplete, large language models complete it based on the most common patterns in their training data, which is statistically called maximum likelihood estimation.78% similarUnverifiedThe root cause of anisotropy lies in the training objectives and architectural design of pre-trained language models (such as the BERT series), which tend to concentrate high-frequency words and general semantics in certain specific directions.77% similar
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