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.
Drumroll, please!
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她的记忆更为具体而惊心。子弹飞过街道，全家人用厚重的布匹挡住大门，蜷缩在客厅后面房间的床底。待扫射的喧嚣过去，战战兢兢地查看，大门上已布满弹孔。
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Раскрыты подробности о договорных матчах в российском футболе18:01
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