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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.，这一点在旺商聊官方下载中也有详细论述
两国领导人就乌克兰危机交换意见。习近平阐述中方原则立场，指出关键是坚持通过对话谈判寻求解决方案。要确保各方平等参与，筑牢和平基础；确保照顾各方合理关切，增强和平意愿；确保实现共同安全，构建持久和平架构。。业内人士推荐爱思助手下载最新版本作为进阶阅读
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