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.
Bafta told the BBC on Friday they looked into the microphone and had assured StudioCanal, which made I Swear, that it was not amplifying the sound in the room or the broadcast.
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Раскрыты подробности похищения ребенка в Смоленске09:27
她指出，台灣雇主若要吸引移工，就必須改善勞動條件；而少子化與長期缺工的趨勢，也意味著台灣需要思考如何培養穩定且具技術的勞動力，並讓移工能在台灣長期安居，甚至成為未來的移民。
Powers of two are wasteful if you have a bunch of arrays that