三角洲最近非常火。 连续几把撤不出来让人非常上火。 这个游戏设计很像人生，想出货就去机密绝密，挑战高难度。 但是高难度地图就是撤不出来。 想撤出来就去普坝普弓，佛系躺平安贫乐道，呆在舒适区。 但那个爆率又不好说。 哥们看我玩的上火，说走，520快到了哥们带你去真实搜打撤。 我问去哪是去零号大坝还是航天基地。 哥们说在酒店楼下。 看到了吗那个垃圾桶，明天晚上就翻它。 ——01—— 前几天各群都流传个图。 你别说，你真别说，人生很多事就是搜打撤。 你出生之后，就要不停的进图，搜索，打架，撤离。 搜，取得各种成绩。 打，克服各种困难。 搜是找投资人，打是忽悠投资，撤是卷钱跑路。 撤，把搜的货带出去，局外人生养成。 到了大学毕业还得拿着自己的人生仓库截图和KD，跟HR说你看我拿了很多大红，KD不差，现在等级够了。 好，恭喜你解锁了航天基地。 搜打撤游戏和吃鸡比，重要的不同就在于局外养成。 你搜索的东西是可以换成钱，钱可以换成装备支持你下次进图的。 你进图的装备需要从局外带。 它甚至连动态难度都有。 进图之后同一难度，它的真实难度和爆率就是不一样的。 有的人进图敌对人机都不会开枪，遇到的对手唐到要打胰岛素，随便搜搜猛猛 ...

Core Insights - The article discusses the limitations of large language models (LLMs) in performing analogy tasks, highlighting that while they may show some capability, they often rely on surface features rather than true abstract reasoning [1][2][29]. Group 1: Analogy Capabilities of Large Language Models - Research indicates that LLMs like GPT-3, GPT-3.5, and GPT-4 can perform analogy tasks but may not truly understand the underlying principles, as they often depend on memorized training data rather than genuine reasoning [2][3][4]. - A study found that when presented with altered analogy tasks using fictional or symbolic alphabets, the accuracy of LLMs significantly decreased, often falling below that of human participants, including children [7][10][13]. - The performance of LLMs in analogy tasks is sensitive to changes in the format of the questions, indicating a lack of robustness in their reasoning abilities [18][20][29]. Group 2: Comparison with Human Performance - In various analogy tasks, human participants, including children, consistently outperformed LLMs, especially when the tasks involved non-standard formats or required deeper understanding [10][13][24]. - The accuracy of LLMs like GPT-4 dropped significantly when faced with paraphrased stories or altered answer options, demonstrating their reliance on specific formats rather than flexible reasoning [25][26][29]. - The findings suggest that while LLMs can achieve high accuracy in controlled tasks, they struggle with variations that require a deeper understanding of context and relationships, unlike human reasoning [29][30]. Group 3: Implications for Future Development - The article emphasizes the need for further research to enhance the analogy-making capabilities of LLMs, suggesting that structured data from traditional literature could provide new avenues for improvement [30][31]. - It advocates for the development of robust testing frameworks to evaluate LLMs' adaptability to new situations, which is crucial for their application in critical fields such as education and healthcare [29][30]. - The potential for LLMs to generate new rules through analogy reasoning frameworks is highlighted as a promising direction for future advancements [30][34].

东四十条资本 . 聚焦股权投资行业人物、事件、数据、研究、政策解读，提供专业视角和深度洞见 | 创投圈有趣的灵魂 将投中网设为"星标⭐"，第一时间收获最新推送 以下文章来源于东四十条资本 ，作者蒲凡 报告似乎就是每个投资人社群最重要的"社交货币"。 作者丨蒲凡 来源丨东四十条资本 前段时间，一篇来自德意志银行的报告频繁出现在投资人的社群里。这篇报告的标题就很吸睛，叫《中国的"斯普特尼克"时 刻》——这是一个典故，概念源自1957年苏联成功发射"斯普特尼克1号"人造卫星，史学家们普遍认为"斯普特尼克1号"的升 空震惊了美国，促使其加速太空竞赛，才有了后来的"阿波罗登月计划"以及"星球大战计划——德银的分析师认为以ChatGPT 为代表的AI产品横空出世，同样带给了中国人强烈的集体刺激，一场将铭记在历史上的变革即将开始。 具体内容就更刺激了。概括起来，通篇有3个重要观点：中国股市即将走出"折价"的周期，并将一举突破高点；贸易战不会对 中国产生根本性的影响；未来全世界将不得不"做多中国"。 但很快，这篇"做多"的报告，自身被"做空"了。有人说，德银业务能力就不行，作为投行近二十年股价下跌了 70% ，相比之 下标普 ...