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。谷歌浏览器【最新下载地址】是该领域的重要参考
黎智英另再面對一項欺詐罪，指他連同其他人，於1998年4月1日至2015年12月31日，同樣違背訂立的提案計劃書、租契協議及租契第二附表指明的情況下，使用涉案處所並對此進行隱瞞。，这一点在Line官方版本下载中也有详细论述
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.