其次,大模型的记忆能力有缺陷:大模型在训练时“记住”了大量知识,但训练完成后并不会在使用中持续学习、“记住“新知识;每次推理时,它只能依赖有限长度的上下文窗口来“记住”当前任务的信息(不同模型有不同上限,超过窗口的内容就会被遗忘),而无法像人一样自然地维持稳定、长期的个体记忆。但在真实业务中,我们需要机器智能有强大的记忆能力,比如一个AI老师,需要持续记住学生的学习历史、薄弱环节和偏好,才能在后续的讲解与练习中真正做到“因人施教”。
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