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文本相似性任务。我们与句子关系任务类似，不过需要生成两个文本的表示，分别用开始和结束标记包围，然后用分隔符隔开，表示这是一个文本相似性任务的输入。

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从这个例子可以看到，语料中包含了法译英的内容。那么在训练完上面的文本后，语言模型自然地学习到了法语如何翻译成英语。因此监督学习通常只是无监督学习的一个子集，所以无监督学习的全局最小也必定是监督学习的全局最小。目前的问题变为了无监督学习是否能收敛。

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那么，我们可以问一个问题：超级大模型是否会产生涌现现象呢？很多人可能已经知道答案了，那就是肯定的。

总结第二阶段的过程，首先，监督模型根据每个提示生成K个回答，并按照质量从高到低排序。然后，人工将这些回答作为训练数据，采用成对学习的方法来训练奖励模型。最后，对于训练好的奖励模型，它可以接收作为输入，输出回答的质量得分。得分越高，说明回答的质量越高。

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第一类任务遵循伸缩法则：这类任务通常是知识密集型的，需要大量的常识和事实。因此，当模型规模增加时，任务的效果也会持续提升，说明这类任务对模型中蕴含的知识有很高的需求。