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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.
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在格式化的数学推理任务上,前者表现不错;但在需要自主探索、动态规划的复杂代理任务上,两者的差距是真实存在的。
Anthropic 放弃核心 AI 安全承诺
,这一点在服务器推荐中也有详细论述
Senior project officer Gary Trimble told BBC Two's Digging for Britain, carnyces "are extraordinarily rare... a boar standard, that's even rarer".
63-летняя Деми Мур вышла в свет с неожиданной стрижкой17:54,这一点在heLLoword翻译官方下载中也有详细论述