市场监管总局答南方周末:不管是卖家、主播还是网红,都不能随便给食品“加戏”

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Жители Санкт-Петербурга устроили «крысогон»17:52

绝对贫困历史性消除,为什么要设立5年过渡期?。Line官方版本下载对此有专业解读

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Graeme Kearns, chief executive of Foundation Theatres, says: ‘Our job in theatre is to absolutely defend the right to tell stories about culture’,详情可参考WPS下载最新地址

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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