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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例如，如果沒有與人面對面互動，我怎麼會知道，在古吉拉特語中，當長輩說「maru loi na pee」（字面意思是「不要喝我的血」）時，其實是在叫我不要煩他們？又或是理解法語中「ça a été」這句字面上意為「事情如其所是」的短語，在對話中卻是表達「一切還不錯」的極萬用回應？