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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.,这一点在谷歌浏览器【最新下载地址】中也有详细论述

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对于移动应用的开发者来说,支持触控屏的 MacBook 在调试方面也大有裨益。这意味着,日后开发移动应用时,可以直接上手在笔记本上进行测试,而不需要另外推送到手机上。,详情可参考WPS下载最新地址