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title: "工具调用(Function Call)" | ||
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docs/02-prompt-engineering/02-chain-of-thought/03-concise-cot.md
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title: "Concise Thought" | ||
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CoT 的长度通常是会影响整体时延,有的项目当中会直接把 CoT 的策略给删除,可是这个通常会影响模型的效果. | ||
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此时你可以有两种策略: | ||
1. 增加你的训练数据 | ||
2. 缩短你的 CoT 长度进而提升模型的效果. | ||
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第一种方法就非常的简单粗暴,不过很可能会破坏你这个模型的通用 chat 能力,除非你的目的就是要构建一个垂类的不具备很强通用 chat 能力的模型。 | ||
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## 参考文章 | ||
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* [[1] Concise Thoughts: Impact of Output Length on LLM Reasoning and Cost](h是) |
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title: "OpenAI 的O1 模型是如何训练的" | ||
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或许训练的模式是这个样子: | ||
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![](./imgs/strawberry-training-mode.gif) | ||
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> 此图片来源于外网一位大佬 |
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