7 ms·
You can’t just prompt your way out of a systemic flaw
by philipwhiuk 1y ago
You can’t just prompt your way out of a systemic flaw
- skeledrew 1y agoWhat's the systematic flaw?
- lottin 1y agoThe fact that it can't count.
- minimaxir 1y agoIf a LLM can get IMO Gold but can’t count, that’s an issue.
- skeledrew 1y agoThat isn't a flaw though. Counting is orthogonal to the functioning of LLMs, which are merely completing patterns based on their training data and available context. If you want an LLM to count reliably, give it a tool.
- mdp2021 1y agoAnything articulate (hence possibly convincing) which could be «merely [guessing]» should either be locked out of consequential questions, or fixed.
- skeledrew 1y agoWe're still on that's just how it works. The LLM isn't aware of any consequence, etc. All it does is complete patterns as trained. And the data contains many instances of articulate question answering. It is for those using the LLM to be aware of its capabilities, or not - be allowed to - use it. Like a child unaware that running their finger on a sharp knife blade will lead to a bad slice; you don't dull the blade to keep the child safe, but keep the child from the knife until they can understand and respect its capabilities.
- mdp2021 1y agoIf your prototype of the «knife» is all blade and no handle, fix it and implement the handle. If the creation is planned, you will have also thought of the handle; if it is a serendipity, you will have to plan the handle afterwards.
- skeledrew 1y agoPretty sure it doesn't matter to the child whether the knife has a handle or not. They'll eventually find a way to cut themself.
- mdp2021 1y agoIt matters to the adult - who is also an user. LLMs do not deliver (they miss important qualities related to intelligence); they are here now; so they must be superseded. There is no excuse: they must be fixed urgently.
- skeledrew 1y agoLLMs deliver pretty well on their intended functionality: they predict next tokens given a token history and patterns in their training data. If you want to describe that as fully intelligent, that's your call, but I personally wouldn't. And adding functionality that isn't directly related to improving token prediction is just bad practice in an already very complex creation. LLM tools exist for that reason: they're the handles, sheaths, sharpeners, etc for the knife. Teach those adults who're getting themselves cut to hold the knife by the handle and use the other accessories that improve user experience.
- jeroenhd 1y agoYou don't need to as long as you don't use LLMs like these in cases where incorrect output isn't of any consequence. If you're using LLMs to generate some placeholder bullshit to fill out a proof of concept website, you don't care if it claims strawberries have tails, you just need it to generate some vaguely coherent crap. For things where factuality is even just a little important, you need to treat these things like asking a toddler that got their hands on a thesaurus and an encyclopaedia (that's a few years out of date): go through everything it produces and fact check any statement it makes that you're not confident about already. Unfortunately, people seem to be mistaking LLMs for search engines more and more (no doubt thanks to attempts from LLM companies to make people think exactly that) so this will only get worse in the future. For now we can still catch these models out with simple examples, but as AI fuckups grow sparser, more people will think these things tell the actual truth.
- handoflixue 1y agoCounterpoint: You absolutely can: https://claude.ai/share/d91f24f8-e174-41c9-bdbc-136ec10b7465 https://claude.ai/share/d91f24f8-e174-41c9-bdbc-136ec10b7465 4 "u" because "b" felt like a bit of a cheat to count in that sentence.