Ударная сила.Как в России создают самые грозные подлодки в мире3 ноября 2023
每天早起，开始写作。你每天做什么，你就成为什么。
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{ 8, 0, 2, 14, 45, 59, 61, 51 },。关于这个话题，Line官方版本下载提供了深入分析
这套说辞在法律上也许站得住脚，但它同时也揭示了一件事：这家公司从未认为自己做错了什么，只是某些手段不够干净。
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.