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.
"The reputation we've built means that artists who wouldn't have seen the North West as a market where they could play one-off shows are now coming and bringing those exclusives to us."
。关于这个话题，夫子提供了深入分析
; while the PLA is still evaluating
Article InformationAuthor, 湯瑪斯·傑曼（Thomas Germain）
社交启蒙社交是她上幼儿园需要面对的一个重要问题，在家都是家人陪着玩，出门玩也是她自己玩的更多一些。3岁以前，都不太愿意跟别的小朋友接触，偶尔遇到大孩子喜欢她的，她也喜欢的会主动跟人玩。其他时候还是会躲着其他小朋友，如果她找别的小朋友玩，有时候她不敢，有时候别的小朋友不愿意跟她玩，她会失落。