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.
圖像加註文字，謝展寰告誡飼主給狗隻餵食人類食物「風險很大」。香港動物權益及福祉協會主席賴嘉敏律師指出，同樣基於選擇，食環署的立法建議表明，獲簽發牌照加註的餐廳將在門口有清楚標識，不喜歡寵物，或者對寵物毛髮過敏的食客，不應強行進入。
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Цены на нефть взлетели до максимума за полгода17:55