Фото: Вячеслав Прокофьев / РИА Новости
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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庞若鸣在七个月前的离职，虽然不至于让苹果的技术大厦倾塌，但确实在一定程度上干扰了其自主研发的节奏。
这句话伴随杜耀豪长大，像一个悬而未决的谜题。他出生在德国，母语是德语，却长着一张东方面孔。在学校，他的名字“Hao”会被同学戏谑地与德语中“殴打”（hauen）的发音联系起来。他名字中的“耀”字，在父母移居德国后，被译成了越南语“Dieu”。杜耀豪猜测，这或许更多地出于实用考虑，但可能也具象征意义，连接着家族背后中越两段交错的历史与漂泊的轨迹。