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.
For a long time fat was seen simply as an inert yellow substance wrapping around our bodies, but now that’s changing. Scientists are beginning to understand that our fat is actually intricate and dynamic, constantly in conversation with the rest of the body. It’s now even considered by some to be an organ in its own right. To find out more about the complex role fat plays in our health, Ian Sample hears from co-host Madeleine Finlay and from Declan O’Regan, professor of cardiovascular AI at Imperial College London
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宜昌人对蜡梅的喜爱，印在了年年岁岁的记忆里。作为传统的年宵花，春节前后，郊区农民剪下自家的蜡梅花枝进城出售，很快就会销售一空。捧着造型独特的蜡梅花枝回家，选个好看的花瓶插上，淡淡的清香弥散开来，新春的氛围一下子就浓了。