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.
相反，她的收入主要來自紅牛、保時捷和蒂芙尼等品牌的代言合作，同時她曾為路易威登和維多利亞的秘密走秀，並與IMG模特經紀公司簽約。
。heLLoword翻译官方下载对此有专业解读
在看过、用过许多个手机/电脑系统级 AI 智能体，也深度使用过「豆包手机」之后，再看这次的 Gemini 智能体，我觉得关于它的讨论不该止于一个「新功能」。
Team Plan: $199/month