“The president reached out to member states and to MEPS, that’s what it means. She reached out to member states and MEPs, and I remind you that the member states as the European Council, endorsed and approved the EU Mercosur agreement and empowered the European Commission to move forward with provisional application.”
This looked much better than what I had before. But it was a bandwidth hog.
。业内人士推荐同城约会作为进阶阅读
Source: https://firebase.google.com/support/guides/security-checklist#api-keys-not-secret
// 核心Map：key=nums2的元素值，value=该元素在nums2中的「下一个更大值」
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.