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.
"We would basically take a screenshot of that house or residence and shoot it over to John and say 'would this house have these bricks inside?'" says Squire.
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她把這些經歷都稱為「荒誕可笑」，但同時意識到正是這些看起來很日常輕松的議題構成了亞裔美國人、亞裔加拿大人的敘事核心。此前，《尚氣》（Shang-Chi）主演劉思慕即因對珍珠奶茶的文化挪用遭議論。