Unverified50% confidenceFactExact time
The capability boundaries, training methods, parameter scales, and evaluation benchmarks of LLMs can all be specifically quantified.
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50%
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Long-term
Relevance
9/12/2026
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Unverified当前主流 LLM 采用指令遵循（Instruction Following）训练方式，会用概率最高的方式补全输出76% similarUnverifiedData annotation quality directly determines the upper bound of model training, and annotation in specialized domains requires the participation of domain experts.75% similarUnverifiedThe LLM training and inference pipeline includes stages such as tokenization, embedding layers, self-attention mechanisms, feed-forward networks, and residual connections.74% similarUnverifiedThe GLM team believes there is a threshold to the role of total parameter count, and that once the model is large enough to contain the world, additional capability comes from effective depth and post-training.73% similarUnverifiedThe model training side has tool support from PyTorch, TensorFlow, and tools such as MLflow, Weights & Biases, and DVC, but the degree of standardization on the inference side is notably insufficient.72% similar
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