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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.
。关于这个话题，heLLoword翻译官方下载提供了深入分析
“Given rising tensions in the region, Chiefs of Mission and embassies at addressee posts must refrain from public statements, interviews, or social media activity that could in any way inflame regional audiences, prejudice sensitive political issues, or complicate US relationships,” the cable said.
但在其中一個案例中，埃及與埃塞俄比亞之間的「戰爭」其實只是關於水壩建設的爭端，並沒有真正的戰鬥需要結束。
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