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.
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While the number of GPs working in the NHS has been increasing over the last year or so, the number of patients per GP is still a fifth higher than it was eight years ago.
It’s two decimal digits long, it’s prime, it’s a palindrome and it’s the number of players in a football team.
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新系统将根据用户操作方式，在触控与传统光标点击之间动态切换界面逻辑。例如，手指点击按钮时，界面会在触点周围弹出更适合触控的菜单；菜单栏项目也会在触控场景下放大，便于手指选择。