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.
而据报道，这已不是元宝第一次「骂人」。今年年初，曾有网友反馈使用该 App 优化代码时，多次收到「滚」「自己不会调吗」 等侮辱性回复，当时官方同样以「小概率下的模型异常输出」为由致歉，并承诺启动内部排查优化。
。safew官方版本下载是该领域的重要参考
ВсеПолитикаОбществоПроисшествияКонфликтыПреступность。safew官方下载是该领域的重要参考
# ANTHROPIC_API_KEY = "sk-ant-..."