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.
'Houston, we've had a problem'
牛犇認為,習近平已將「自我革命」作為自己第三個任期的組織原則。這場結合了反腐敗、意識形態灌輸和政治紀律的運動,帶來了前所未有的清洗,重塑了黨國體制和軍隊。自我革命是他解決「在沒有民主的情況下實現問責」這一難題的方案。,更多细节参见91视频
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