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.
Москвичей предупредили о резком похолодании09:45
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這位在加州出生、由美國父親與中國母親撫養長大的運動員，曾就讀舊金山私立學校，目前正從斯坦福大學暫停學業休假。
The Taliban government says they were "retaliatory operations" - a response after "Pakistani military elements carried out an incursion into Afghan territory, violated Afghan sovereignty, and caused the deaths of several civilians, including women and children".
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“我国小麦消费量年增速为1.1%—1.6%，到2030年，单产必须提高10%以上才能满足需求。”在刘录祥眼里，保障小麦口粮绝对安全，提单产势在必行。而种子，则是关键的增产密码。分子标记、快速育种等现代育种技术，助力高效精准地培养出“逆商”更高的优质小麦。
非法携带枪支、弹药或者弩、匕首等国家规定的管制器具进入公共场所或者公共交通工具的，处五日以上十日以下拘留，可以并处一千元以下罚款。。关于这个话题，搜狗输入法下载提供了深入分析