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.
Она отметила, что потепление будет неравномерным. На погоду в Москве будет влиять циклон в его теплой части, поэтому с 28 февраля по 2 марта будет теплая погода. Затем столица окажется в холодной части циклона, которая приведет к снижению температуры. В течение следующей недели она может упасть до минус 15 градусов.
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Chris Damant/Bernwood Ecology
庞若鸣曾被视为“苹果AI脊梁”的核心天才，他在Meta的工位还没坐热，就决定放弃那份令人咋舌的过亿期权激励，毅然转身投奔奥特曼麾下。，更多细节参见WPS下载最新地址
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Maggie姐略微有点不耐烦。傍晚6点不到，水车屋还没开始营业，她必须和其他客人一样等在门外。作为老客，她并不满意这样的待遇，尤其是她发现那些翻看菜牌、赶着饭点来的客人不过是要去楼下两层打发他们的晚餐，立刻就把她从当年的盛况里拉了回来。这几年，夜总会生意不景气，水车屋也跟着走下坡路，早就在招揽中低消费的客人，原先的老客也不见踪影。