English(EN) Anyone who spent some time with and investigating # LLM based # AI knows that it's nothing other than an RNG that happens to be right sometimes. But most output

用户认为LLM是随机数生成器，并举例说明其在编程任务中的失败

作者 PulseAugur 编辑部 · [1 个来源] · 2026-06-11 07:52

一位Mastodon用户对当前大型语言模型（LLM）的能力表示怀疑，将其比作一个偶尔能产生正确输出的随机数生成器。用户表示，大多数LLM的输出都是“垃圾”，并分享了一个关于涉及多个if-then检查的简单编程任务的轶事，即使是先进的本地模型也无法解决，并以AI无法正确计算括号为例。 AI

影响用户对LLM的能力表示负面看法，强调其在逻辑推理和计数方面的局限性。

排序理由用户在社交媒体上发表的关于LLM的观点文章。

在 Mastodon — sigmoid.social 阅读 →

观点

AI 生成摘要 · Google Gemini · 来自 1 个来源。我们如何撰写摘要 →

报道来源 [1]

Mastodon — sigmoid.social TIER_1 English(EN) · [email protected] · 2026-06-11 07:52

Anyone who spent some time with and investigating # LLM based # AI knows that it's nothing other than an RNG that happens to be right sometimes. But most output

Anyone who spent some time with and investigating # LLM based # AI knows that it's nothing other than an RNG that happens to be right sometimes. But most output is trash anyways. I have a simple programming task that even the best local models can't solve yet. It's about multiple…

报道来源 [1]

Anyone who spent some time with and investigating # LLM based # AI knows that it's nothing other than an RNG that happens to be right sometimes. But most output

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