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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.
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In another case, the ChatGPT user asked the AI agent to draw up a multi-part plan to denigrate the incoming Japanese prime minister, Sanae Takaichi, in part by fanning online anger about US tariffs on Japanese goods. ChatGPT refused to respond to the prompt, according to OpenAI. But in late October, as Takaichi took power, hashtags emerged on a popular forum for Japanese graphic artists attacking her and complaining about US tariffs, according to OpenAI.
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12月22日，彩虹星球就本次诉讼召开说明会。受访者供图