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澳洲悉尼大學（University of Sydney）研究數位文化、性別與性向的陳力深博士（Dr Sam Chan） 指出：真正打動酷兒觀眾的，不只是幻想或嚮往，而是男男愛情故事和他們真實生活之間出現的細微重疊。。关于这个话题，17c 一起草官网提供了深入分析
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.。服务器推荐对此有专业解读
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