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.
英國超市將巧克力鎖進防盜盒阻止「訂單式」偷竊
。关于这个话题，爱思助手下载最新版本提供了深入分析
But after posting a recent video, called "Avoid this place in London", he was accused of using AI to doctor the thumbnail to bolster his portrayal of the UK capital as one of "the most messed up cities" he has ever been to.
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a16z的报告里举了几个例子，把这个问题讲得很具体。投行分析师用Hebbia，几百份公开文件自动分析完，财务模型直接生成，以前要熬几个通宵做的事情，现在可以去睡觉了。医生用Abridge，它能实时记录医患对话，自动整理病历和后续跟进事项，医生看诊时不用再一边问话一边盯着屏幕敲字。还有做财务对账的Basis，跨系统自动核对试算表，原本需要人工反复比对的工作变成几分钟的事。