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(二)超过询问查证的时间限制人身自由的;
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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1. 先有密度,再有规模国内某AI独角兽在A轮融资后,急于扩大规模,短期内扩招至150人,导致核心人才密度被快速稀释,决策效率下降、部门内耗增加,后期不得不收缩团队至50人,重新聚焦核心业务。。关于这个话题,51吃瓜提供了深入分析
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