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.
这对企业意味着什么？过去，企业可以默认没写就算覆盖，至少在谈判桌上还有争议空间；现在，行业正在把争议空间主动收拢。Barron’s报道显示，越来越多商业险承保方开始在责任险、董责等条款中加入更广泛的AI排除，原因不是他们不理解AI，而是他们理解得太清楚：一旦AI的错误以同一底座、同一模型接口、同一供应链形式被大规模复用，保险面对的就不再是一个事故，而可能是同源事故的成片发生。
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首次启动会拉大量镜像，时间可能较长。，这一点在旺商聊官方下载中也有详细论述