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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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敢於成為唯一參加三項賽事的女性選手，不應受到懲罰。在某項賽事晉級決賽，不應使我在另一項賽事中處於劣勢。
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The video includes fabricated audio of Tkachuk referring to Canadians as “maple syrup eating (expletive),” with the expletive bleeped out. The video carries a note saying it “contains AI-generated media.”。同城约会对此有专业解读
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