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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In defending herself and her business, Kennedy returns to the idea that seeking help with parenting—and paying for it—is not all that different from pursuing coaching of other kinds. “I’ve never heard anyone say that executive coaches make CEOs anxious, right? I don’t hear anyone saying money managers make people anxious with their finances, or basketball coaches or sports psychologists make athletes anxious in those fields.”，详情可参考heLLoword翻译官方下载
市民小心翼翼地走在冰面上，冰面上坑坑洼洼净是冰碴儿。