3 ms·
What about general knowledge you can get out of it before hallucinations start?
by rnewme 1mo ago
What about general knowledge you can get out of it before hallucinations start?
- walrus01 1mo agoIt did OK on schlongbench v1.0 (test of a specific niche word that doesn't make it into smaller LLMs) but it sure does love to count words https://pastes.io/r8F1AY8h https://pastes.io/r8F1AY8h
- verdverm 1mo agoI do not rely on any LLM of any size for general knowledge baked into the weights, they all hallucinate and that is the wrong way to hold them imo I think there is some merit in that smaller models cannot memorize so much of the training data, i.e. that they are less likely to do copyright infringement, and by analogy not having memorized SDK / API surfaces that have since changed from the training data
- walrus01 1mo ago> I do not rely on any LLM of any size for general knowledge baked into the weights You have to rely on it to a certain level for agentic/coding work, presuming that's the general subject we're talking about here... For instance I recently encountered a project where it would have been a lot worse if the LLM didn't already know "what is" xterm.js and a bunch of its associated npm-related/node related software. If it was still smart but had to google and find results for everything it would have been a lot more time consuming and risked sending it down a wrong path.
- verdverm 1mo agofor sure, there is a minimum size and knowledge base that is required to be useful at the same time, search may find newer or better alternatives, and you can always specify specific technologies you want to use, I typically do this when starting a new project
- stymaar 1mo agoStoring general knowledge in VRAM has always been a dumb idea in the first place.
- Balinares 1mo agoQwen 3.8 27B beats Opus, Fable and GPT 5.6 by a comfortable margin on the AA-Omniscience Hallucination Rate benchmark.