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.
但在1993年藤森宪法中，这一概念未被明确定义。在政党碎片化与政治极化背景下，自2016年以来，它逐渐演变为国会弹劾总统的常规工具。条文未改，含义却已改变，这成为秘鲁制度危机的重要法律根源之一。
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週一空中技巧決賽期間，見她與前國際奧委會主席托馬斯·巴赫（Thomas Bach）並肩觀賽。
颠覆者正在登场与所有FIC创新药一样，尽管Vosoritide率先打开了ACH靶向治疗的大门，但其正面临着后来者的加速追赶与围攻。。服务器推荐是该领域的重要参考