Предсказана реакция стран ЕС на план ускоренного вступления Украины14:48
对于重度大模型的用户来说，模型切换的成本是非常高的。，详情可参考下载安装 谷歌浏览器 开启极速安全的 上网之旅。
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Her venture started in late 2023, when she took the crossing guard job and began writing a monthly “cloud report,” which she posted on social media. The report included snippets of her day — like photos of a handwritten thank-you note from a child and snow falling on a store. Her followers were eager to see more, and would even reach out if she forgot to post for a month.
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.