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.
Grammarly is a better option for accuracy, whereas ProWritingAid is better for handling fragmented sentences and dialogue. It can be quite useful for fiction writers.。业内人士推荐WPS下载最新地址作为进阶阅读
第三十四条 国务院核工业主管部门会同有关部门制定核技术应用产业发展指导意见。。夫子对此有专业解读
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第三条 扰乱公共秩序，妨害公共安全，侵犯人身权利、财产权利，妨害社会管理，具有社会危害性，依照《中华人民共和国刑法》的规定构成犯罪的，依法追究刑事责任；尚不够刑事处罚的，由公安机关依照本法给予治安管理处罚。