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.
Цены на нефть взлетели до максимума за полгода17:55
，这一点在Line官方版本下载中也有详细论述
不过这种方案不是完美的——如果只点亮那些窄角发光像素，屏幕的分辨率和亮度会受到一些细微影响。
从生活品质悄然升级，到农业农村现代化渐行渐近，正是在新供给和新需求良性互动中，提升发展的含金量、老百姓的幸福感。
Adrienne MurrayTechnology Reporter, Esbjerg, Denmark