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.
尤其让我惊艳的，是它在每页备注中生成的演讲词：内容口语化，且熟练使用了「在正式开始之前」、「接下来」等衔接词。这甚至让我感到一丝被硅基生物支配的恐惧：也许未来在台上的某次宣讲中，我们已分不清演讲者是在阐述自己的思想，还是仅仅充当了 AI 的「肉身代言人」。
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牛犇認為，更可信的解釋是北京為了正當化對張又俠的清洗，編造了最嚴重的罪名，即便真實原因只是嚴重的腐敗和不忠。