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.
依然是我们熟悉的 5000mAh 电池、无缘蓝牙功能的 S-Pen，以及一块 6.9 寸的旗舰级 2K 屏。。爱思助手下载最新版本对此有专业解读
，详情可参考heLLoword翻译官方下载
派拉蒙的方案包括每股 31 美元现金、70 亿美元监管分手费，以及承担华纳需向 Netflix 支付的 28 亿美元终止费。，更多细节参见WPS下载最新地址
// 入栈后栈仍保持单调递增（因为小的都被弹走了）
随后经浦北陈皮企业负责人引荐，记者见到多位专程从新会赴浦北采购的商家，他们坦言同时经营新会皮与浦北皮，外地“工艺皮”因价格低廉成电商供货重要选择。