Unverified50% confidenceFactExact time
Self-supervised learning does not require manual annotation; instead, it constructs supervision signals by predicting the next token from the given preceding context, which is the key to scaling up LLM training.
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10/17/2026
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UnverifiedDuring the pre-training phase, LLMs optimize token prediction probability through maximum likelihood estimation, lacking an inherent mechanism to 'know what they don't know,' and there is no necessary correlation between confidence and accuracy.79% similarUnverifiedThe LLM training and inference pipeline includes stages such as tokenization, embedding layers, self-attention mechanisms, feed-forward networks, and residual connections.76% similarUnverifiedThe training objective of LLMs is to generate fluent, coherent, contextually appropriate text, not to guarantee factual accuracy75% similarUnverified预训练阶段模型通过自监督学习方式（通常是下一个 token 预测任务）在海量文本语料（通常达数万亿 token 规模）上进行训练74% similarUnverifiedLISCO adopts a self-supervised training approach, using the degree to which KKT conditions are satisfied as the objective, without requiring pre-computed optimizer solutions as labels.74% similar
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