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.
新时代以来，以“功在当代、利在千秋”之志，开展生态文明建设一系列开创性工作；站在“为民族复兴立根铸魂”的高度，推动中华优秀传统文化创造性转化、创新性发展；秉持跳出治乱兴衰“历史周期率”的清醒，纵深推进全面从严治党……
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Цены на нефть взлетели до максимума за полгода17:55
For a game to legally be counted as "gambling" in most jurisdictions, it has to pass a three-part test: a player has to pay money (1) for an outcome that's materially determined by chance (2) in the hopes of receiving something of value (3). While buying a key to a loot box in a Valve game easily passes those first two tests, New York's legal case will likely hinge on whether the random cosmetic items players get from those loot boxes constitute "something of value" for statutory purposes.。关于这个话题，旺商聊官方下载提供了深入分析