Thread

1. walrus01 · 1785887768 · focus · HN ↗

   I wish that "small" LLMs would stop being confidently very incorrect. Admittedly this is a bit of an intentionally esoteric test, but the confident way in which it presents a totally incorrect answer is a bit concerning.

   "please write 250 words on the etymology and history of the word schlong"

   <a href="https:&#x2F;&#x2F;pastes.io&#x2F;uhshFgn4" rel="nofollow">https:&#x2F;&#x2F;pastes.io&#x2F;uhshFgn4

   The actual origin of the word is from middle high German and Yiddish-speaking Ashkenazi Jewish communities.

   For comparison qwen 3.6 35B A3B does perfect on this and will give a solid description of the word&#x27;s real origins and how it has made it into casual profanity&#x2F;vulgarity as used in US English, and even mentions specific stand-up comedians and famous public figures of specific ethnic&#x2F;religious origin in the US NE who introduced it into wider use.

   Ask it for something that&#x27;s not a narrow niche scientific or technical field, but something that would be less common to make it into a 20B size model, and see just how it does.

   chat test link: <a href="https:&#x2F;&#x2F;chat.deepgrove.ai&#x2F;">https:&#x2F;&#x2F;chat.deepgrove.ai&#x2F;

   1. thomastjeffery · 1785937929 · focus · HN ↗

      They are not incorrect. They don&#x27;t even hold a position in the first place.

      An LLM continues the prompt it is given. What is more likely to come after a question? An answer, not an &quot;oh sorry I&#x27;m not sure&quot;. Sure, you could make the latter more likely, but then the model would be unusable. Larger models simply contain more answers, more ways to stumble into them, and a granular enough geography to stay on the trail.

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