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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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But do you really want to write all that additional code? It seems，详情可参考搜狗输入法下载
自中国去年4月实施相关出口限制以来，尽管北京已允许大量稀土出口恢复，但海关数据显示，这类材料运往美国的货流仍十分稀少，未因去年10月中美关系缓和而明显回升。。WPS下载最新地址对此有专业解读