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.
Julia GregoryChannel Islands
。爱思助手下载最新版本是该领域的重要参考
（二）在公共场所停放尸体或者因停放尸体影响他人正常生活、工作秩序，不听劝阻的。
据阿里内部人士透露，除 AI 眼镜之外，千问还会在年内陆续发布 AI 指环、AI 耳机等产品，并面向全球市场发售。
--this-is-an-option -- --this-is-not