I wanted to test this claim with SAT problems. Why SAT? Because solving SAT problems require applying very few rules consistently. The principle stays the same even if you have millions of variables or just a couple. So if you know how to reason properly any SAT instances is solvable given enough time. Also, it's easy to generate completely random SAT problems that make it less likely for LLM to solve the problem based on pure pattern recognition. Therefore, I think it is a good problem type to test whether LLMs can generalize basic rules beyond their training data.
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該用戶還要求ChatGPT協助潤色一份範圍更廣的定期行動報告，目的是「發現、施壓、干擾及噤聲」異見人士。
The implication is that confusable detection systems should be aware of the rendering context. A warning that says “this string contains a confusable character” is less useful than one that says “this string contains a character that is pixel-identical to its Latin counterpart in the font your users will see.”