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2024年4月，习近平总书记在重庆考察时，拿“窝窝头”和“精面细面”打比方，论述煤炭等能源行业的发展：“先吃饱肚子再吃好。我们要实事求是，既不能放慢绿色低碳发展步伐，也不能太理想化，首先要保证能源供应。”，详情可参考safew官方版本下载
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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.