LLM 究竟是让不可能成为可能，还是只是让事情更快？[D]

Have LLMs actually made the impossible possible, or just made things faster? [D]

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有用户提出疑问：LLM 目前多数应用（如编码、规划）只是加速已有任务，而非像火与电那样创造全新可能。该讨论援引 Searle 的“中文屋”思想实验，质疑概率模型在缺乏理解的情况下能否带来效率之外的突破，并认为假设生成是较有前景的方向，同时征集 LLM 解决以往方法无法解决问题的真实案例或论文。

正文

I’ve been wondering what LLMs have fundamentally changed.

Inventions like fire and electricity didn’t just save time. They made entirely new things possible, like cooking and refrigeration.

But most LLM applications I see, like coding and planning, just make existing tasks faster.

Searle’s Chinese Room also comes to mind. If LLMs are fundamentally probabilistic models that generate convincing answers without necessarily understanding them, can they really lead to breakthroughs beyond efficiency?

One thing I find promising is hypothesis generation. LLMs can suggest ideas I wouldn’t have thought of myself.

So, has anyone seen LLMs solve problems that previous methods simply couldn’t? What specific capability made that possible?

Would love to hear real examples or papers. Thanks for sharing!

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