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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.
For anyone who has been following the soap opera unfolding between Netflix and Paramount Skydance over the past few months in their financial brinksmanship to acquire Warner Bros. Discovery, the saga may be nearing its end. Today, WBD said its board of directors have determined that the latest offer from Paramount Skydance amounted to the better proposal. The media outfit gave Netflix four business days to match Paramount's terms, but the streamer didn't waste any time in declining to raise its own bid.。旺商聊官方下载对此有专业解读