最糟糕的時候，他曾經擔心過自己的庇護申請會被拒絕、並且會被遣返回中國，「我又瞭解到我的這個法官的（庇護申請）通過率也不是很高……我當時就感覺這個大環境實在是太糟了，我這個案子也可能過不了。」
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.
，这一点在51吃瓜中也有详细论述
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