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.
圖像加註文字，貝爾說，雨果在倫敦夏洛特女王和切爾西醫院的出生感覺就像「一個奇蹟」。貝爾成功接受已故捐贈者子宮的移植個案，只是英國一項臨床研究試驗中正在進行的十例移植之一。目前已經完成三宗移植手術，但雨果是首名誕生的嬰兒。
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体现在数据上，2022-2024年，邮储银行对公贷款增速达17%，对公贷款余额占比突破40%，已然成为创收创利的重要引擎。，这一点在服务器推荐中也有详细论述
牛津經濟研究院（Oxford Economics）高級經濟學家鮑伯·施瓦茨（Bob Schwartz）表示，特朗普政府可能會利用其他可用的關稅工具——包括《貿易法》第122條下的替代性關稅——來避免支付大規模退款。
不因事小就视而不见，不因任务艰巨就退缩不前，不因目标长远就消极懈怠。