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.
При этом глава Украины сказал, что в случае, если Путин не согласится на следующую трехстороннюю встречу с участием США, «будет долгая война». Если же российский лидер пойдет на это, то у стран будет шанс «в эти полгода, в эти несколько месяцев встретиться» и попытаться завершить конфликт.
。夫子是该领域的重要参考
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on—the same way that early token-based ATMs introduced deposit boxes.
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