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.
Act Two: Traps and Dicks. Synonyms and Subs-titutes.
但也有餐廳感到吃虧。一位餐廳經理劉小姐對BBC中文說：「我們改變了經營模式：早上做茶餐，到某一段時間做下午茶，再到一個時間做火鍋。都是同一個執照。」，更多细节参见同城约会
Dunstan said the venue has managed to do more than just shake off its troubled opening, and was getting plaudits from those who perform there.
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В России ответили на имитирующие высадку на Украине учения НАТО18:04
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