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.
Listen: The Food Programme, The Honey Trap
，推荐阅读爱思助手下载最新版本获取更多信息
chunks.push(chunk);
要在这样一群没有明显短板的对手中突围，仅凭「性价比」三个字已经很难奏效了。常规的打法，很难让零跑从这些巨头口中抢到足够的份额来实现那 105 万辆的野心。
从一场场重要会议到一次次国内考察调研，习近平总书记的一系列重要论述，成为各地推进过渡期工作的根本遵循和力量源泉。