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.
Фото: Melnikov Dmitriy / Shutterstock / Fotodom
，这一点在电影中也有详细论述
在谷歌决定收购DeepMind的时候，中间又插出了Facebook的扎克伯格。当时AI火了起来，扎克伯格也很着急，也急于想找到一个团队来做这个事情。哈萨比斯和苏莱曼，用马斯克、用扎克伯格作为手中的牌来对付谷歌，希望能谈出更高的价钱，这是他们的博弈策略。
On the podcast today: was this the night Arsenal won the title? An unconvincing victory at Brighton thanks to a deflected goal that should have been saved and dropped points for Manchester City at the Etihad see the Gunners go seven clear at the top.
По словам эксперта, удобрения дорожают, так как они остаются запертыми в Ормузском проливе, который Иран перекрыл после начала боевых действий на Ближнем Востоке.