print(f"Completed! Processed {len(self.seen_urls)} pages")
hollywoodreporter.com
，更多细节参见雷电模拟器官方版本下载
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.
他的演說開場使用熟悉的口號。「我們的國家回來了，」他說。這是世界上「最熱門」的國家。在某個段落，他指責民主黨製造了「負擔能力危機」後補充：「我們做得非常好。」
:first-child]:h-full [&:first-child]:w-full [&:first-child]:mb-0 [&:first-child]:rounded-[inherit] h-full w-full