In 1942, at the height of British industrial war mobilization, an unlikely cohort scavenged the nation’s coastline for a precious substance. Among them were researchers, lighthouse keepers, members of the Royal Air Force and the Junior Red Cross, plant collectors from the County Herb Committee, Scouts and Sea Scouts, schoolteachers and students. They were looking for fronds and tufts of seaweed containing agar, a complex polysaccharide that forms the rigid cell walls of certain red algae.
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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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抓落实，是衡量领导干部党性和政绩观的重要标志。