This poses a problem, Boeldt said, as any attempt to stop children from using certain terms will just invent and breed a new set of vocabulary that in turn will then force a new set of attempts to monitor that language, inevitably becoming a never-ending cycle.
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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Despite the physical and emotional demands of the job, McKenzie says nothing beats the incredible experiences he's had - as well as the satisfaction of contributing to environmental research.