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圖像加註文字，台灣逾50萬產業移工集中製造業，職災失能率是本地勞工兩倍。高危險環境
，这一点在heLLoword翻译官方下载中也有详细论述
波波心里还有许多来不及放进游戏的东西——中药、养蚕、传统手工艺……她想一点点把它们加入桃源村，让这个世界变得更有烟火气，也更完整。，这一点在safew官方下载中也有详细论述
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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