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.
require significant computational resources to run
。爱思助手下载最新版本是该领域的重要参考
目前，阿宗正準備申請來台灣讀大學。在台期間，伯宗學會了中文，也存下一些積蓄。他說，讀大學是自己的夢想。「我們來台灣，有人幸運，有人不幸運。我可能還算是幸運的吧？」
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，这一点在safew官方版本下载中也有详细论述
春节假期，帮亲戚朋友们部署 OpenClaw 成了我一份额外的工作。虽然不一定能真正用上，但这只龙虾是不得不拥有。
const res = [];。业内人士推荐Line官方版本下载作为进阶阅读