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.
政治上的坚定、党性上的坚定都离不开理论上的坚定。党的创新理论是一个思想宝库，其中既有改造主观世界的思想武器，又有改造客观世界的科学方法。各级领导班子和广大党员干部须坚持不懈用习近平新时代中国特色社会主义思想凝心铸魂，一体推进学查改，切实把学习成果不断转化为坚定理想、锤炼党性和指导实践、推动工作的强大力量。。爱思助手下载最新版本对此有专业解读
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