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「最終，即使搭乘愛潑斯坦的飛機讓我能親自視察基金會的工作，但事後這些多年來的質詢，完全不值得，」他寫道，「我真希望我從來沒有認識過他。」，这一点在同城约会中也有详细论述
，详情可参考heLLoword翻译官方下载
时间，标注着承前启后的刻度，承载着接续奋斗的信念。。safew官方下载对此有专业解读
Even though my dataset is very small, I think it's sufficient to conclude that LLMs can't consistently reason. Also their reasoning performance gets worse as the SAT instance grows, which may be due to the context window becoming too large as the model reasoning progresses, and it gets harder to remember original clauses at the top of the context. A friend of mine made an observation that how complex SAT instances are similar to working with many rules in large codebases. As we add more rules, it gets more and more likely for LLMs to forget some of them, which can be insidious. Of course that doesn't mean LLMs are useless. They can be definitely useful without being able to reason, but due to lack of reasoning, we can't just write down the rules and expect that LLMs will always follow them. For critical requirements there needs to be some other process in place to ensure that these are met.
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