Unverified50% confidenceFactExact time
The capabilities and behavior of large language models are deeply dependent on training data.
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50%
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Long-term
Relevance
9/11/2026
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UnverifiedTraining data in the instruction tuning phase contains a large volume of creative writing, business email templates, and role-playing dialogue samples, enabling the model to master various textual formats79% similarUnverifiedDeepMind's Chinchilla scaling law (from the 2022 paper "Training Compute-Optimal Large Language Models") shows that model performance depends on the optimal ratio of parameter count to training token count, with the optimal training volume being approximately 20 times the number of parameters78% similarUnverifiedLarge language models possess Emergent Abilities, whereby after the model parameter scale and training data volume reach a certain threshold, capabilities not explicitly required by the training objective suddenly appear, such as a sense of humor, use of metaphor, and situational empathy77% similarUnverified大语言模型对自身身份的报告依赖训练数据和系统提示中的信息注入，并非对运行时环境的直接读取，因此让模型自述身份存在技术局限76% similarUnverifiedLarge-scale multitask learning enables large language models to develop cross-domain emergent abilities—performing well on tasks not explicitly taught during training.76% similar
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