「我們常聽到有人說：『我跟人相處完全沒問題。』但後來他們才發現，自己其實不習慣與他人共享空間。」
Thinking Step 3: 推理第二个人。他听到了第一个人的话，推断出第一个人想要。如果第二个人自己不想要，他会说“不”。他说“不知道”，说明他自己也想要，但他不确定第三个人。
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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.
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