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.
Parser--Crawler: urls[], next_url
，详情可参考谷歌浏览器【最新下载地址】
Yet conspiracy theories, especially on the right, have swirled for years around the Clintons and their connections to Epstein and Maxwell, who argues she was wrongfully convicted. Republicans have long wanted to press the Clintons for answers.
申琦团队指出，老年人大模型提问的文本内容中既有针对生活需要的知识性提问，也有基于情感倾诉与慰藉的问答。这意味着，和我们一样，老年人对AI有着工具和情感的双重诉求。