Unverified50% confidenceOpinionExact time
OpenAI argues that the training process constitutes transformative use of large volumes of data, and that the model does not simply copy original text but learns statistical patterns and linguistic structures.
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50%
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Long-term
Relevance
9/12/2026
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VerifiedWhen 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.77% similarVerifiedOpenAI's o1 series models internalize Chain of Thought as a core mechanism of model training, using reinforcement learning to teach the model to automatically unfold detailed reasoning processes.77% similarUnverifiedOpenAI's research papers demonstrate that in domains such as mathematical reasoning, carefully designed synthetic data can surpass the training effectiveness of human-annotated data.77% similarUnverified成对数据（转换前位图与转换后矢量图一一对应）在监督学习中被称为标注数据，是训练AI模型理想的数据形态76% similarUnverifiedIn-context learning was first observed in large language models, and its capability fundamentally stems from the diversity of pretraining data.75% similar
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