[kleiba]

Yes, but the quality of the output leaves to be desired. I just asked about some sports history and got a mix of correct information and totally made up nonsense. Not unexpected for an 8k model, but raises the question of what the use case is for such small models.

[kgeist]

8b models are great at converting unstructured data to a structured format. Say, you want to transcribe all your customer calls and get a list of issues they discussed most often. Currently with the larger models it takes me hours.

A chatbot which tells you various fun facts is not the only use case for LLMs. They're language models first and foremost, so they're good at language processing tasks (where they don't "hallucinate" as much).

Their ability to memorize various facts (with some "hallucinations") is an interesting side effect which is now abused to make them into "AI agents" and what not but they're just general-purpose language processing machines at their core.

[eternauta3k]

Would be nice to point this at (pre-LLM) Wikipedia and fill out Wikidata!

[djb_hackernews]

You have a misunderstanding of what LLMs are good at.

[cap11235]

Poster wants it to play Jeopardy, not process text.

[paganel]

Not sure if you're correct, as the market is betting trillions of dollars on these LLMs, hoping that they'll be close to what the OP had expected to happen in this case.

[IshKebab]

I don't think he does. Larger models are definitely better at not hallucinating. Enough that they are good at answering questions on popular topics.

Smaller models, not so much.

[kleiba]

Care to enlighten me?