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.
The deal was announced by the Federal Trade Commission (FTC) and a group of 11 states, of leaving the drivers on its Spark Driver app tens of millions of dollars out of pocket.
。WPS下载最新地址对此有专业解读
Continue reading...。关于这个话题，同城约会提供了深入分析
有趣的是，Infigratinib最初于2021年获批用于胆管癌，后因适应症开发策略调整而主动撤回。如今，其有望在ACH赛道迎来“第二春”。基于其疗效和口服便利性，BridgeBio在今年的JPM大会上预测，该药Infigratinib将占据ACH治疗市场50%以上的份额。
Thanks, Andrew! We're impressed with all the thought you put into your setup!