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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.，更多细节参见搜狗输入法下载
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工傷後，雇主不跟他續約，但他才剛還清債務，正要開始存錢，沒有離開台灣的理由。他因工傷紀錄使求職困難，輾轉15家仲介公司才重新找到工作。如今，他在一家拖鞋工廠任職，採按件計酬，每天工時長達15小時，月休僅有兩天。。关于这个话题，Line官方版本下载提供了深入分析