更重要的是，被给予厚望的日产 N6 和 N7 均表现不佳，N6 仅售出了 3012 辆，上市 4 个月以来累计售出 10399 辆，而风光一时的 N7 销量也在持续下行，1 月份仅售出了 978 辆。
蓋茨的發言人先前已回應，稱這些指控「完全荒謬且不真實」。
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Фото: Kuba Stezycki / Reuters
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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