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On Elephants in the Room: Trusted Execution Environments，推荐阅读safew官方下载获取更多信息
。关于这个话题，夫子提供了深入分析
Фото: Михаил Воскресенский / РИА Новости。Line官方版本下载是该领域的重要参考
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.
Dithering in its simplest form can be understood by observing what happens when we quantise an image with and without modifying the source by random perturbation. In the examples below, a gradient of 256 grey levels is quantised to just black and white. Note how the dithered gradient is able to simulate a smooth transition from start to end.