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.
突出一个“实”字，就要避免“虚”，就要力戒形式主义，力戒“面子工程”。，更多细节参见WPS下载最新地址
。关于这个话题，51吃瓜提供了深入分析
self.writer.writerow({
Раскрыты подробности о договорных матчах в российском футболе18:01，推荐阅读夫子获取更多信息
人 民 网 版 权 所 有 ，未 经 书 面 授 权 禁 止 使 用