从 512px 的配图到 4K 级别的超高清海报，它都能拿捏。输入一段关于「重庆老火锅」的提示词，它能生成一张赛博朋克风的雨夜街景，湿漉漉的柏油路上，红蓝霓虹灯的倒影和「24 小时营业」的招牌都细致入微。
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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backpressure: 'strict'
She also criticised the PM for citing his own experience as a father of two teenage children on social media, arguing that this did not make him an expert on the subject and that his family were "sheltered" compared to others.
The Baked Good Quadrant: The items here are only breakfasts by convention. Any of them could be served at other meals, and often are.