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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.。关于这个话题,快连下载安装提供了深入分析
平台也在回应相关质疑。“灵师”曾向平台建议引入心理专家等专业资源,为未成年人提供心理疏导和矛盾化解服务。平台方面表示,点赞与主页功能的初衷是为了让用户更好地展示自己,并非盈利。“他们或许没料到会衍生出这么多问题,但应该积极回应处理。”“灵师”说。