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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在这样的语境下，如果速度过快，这种重量就会消失，音乐会变得过于“正常”，而这是我不希望发生的。当然，速度也有边界，不能慢到失去乐句的线条和整体的流动性，但这个界限在哪里，很大程度上取决于个人的判断，以及他自身对时间和节奏的感受。，这一点在Safew下载中也有详细论述
Ранее ветеринар Андрей Руденко призвал россиян не подкармливать диких животных зимой. По его словам, это может нанести им вред. Эксперт подчеркнул, что в особенности нельзя предлагать животным хлеб, сладкое, соленое и переработанные продукты.
Что думаешь? Оцени!