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.
在这个示例中，我们使用最成熟的 proto3 语法。。heLLoword翻译官方下载是该领域的重要参考
，这一点在WPS下载最新地址中也有详细论述
Дания захотела отказать в убежище украинцам призывного возраста09:44
Best perk of my Bafta success? A free sofa。safew官方下载对此有专业解读
“把推进乡村全面振兴作为新时代新征程‘三农’工作的总抓手”“一步一个脚印，把乡村全面振兴的美好蓝图变为现实，为实现农业农村现代化、建设农业强国奠定基础”……