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There are use-cases where hallucinations simply do not matter. My favorite is finding the correct term for a concept you don't know the name of. Googling is extremely bad at this as search results will often be wrong unless you happen to use the commonly accepted term, but an LLM can be surprisingly good at giving you a whole list of fitting names just based on a description. Same with movie titles etc. If it hallucinates you'll find out immediately as the answer can be checked in seconds.

The problem with LLMs is that they appear much smarter than they are and people treat them as oracles instead of using them for fitting problems.

by skydhash3 days ago|

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Maybe I read too much encyclopedia, but my current workflow is to explore introductory material. Like open a database textbook and you'll find all the jargon there. Curated collection can get you there too.

Books are a nice example of this, where we have both the table of contents for a general to particular concepts navigation, and the index for keyword based navigation.

by fao_3 days ago|

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Right! The majority of any 101 book will be enough to understand the jargon, but the above poster's comment looks past the fact that often knowing what term to use isn't enough, it's knowing the context and usage around it too. And who's to know the AI isn't bullshitting you about all or any of that. If you're learning the information, then you don't know enough to discern negatively-valued information from any other kind.

by alpaca1282 days ago|

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I thought it's clear from my comment that I don't rely on AI for information but to find out how to even search for that information.

> The majority of any 101 book will be enough to understand the jargon

A prompt is faster and free, whereas I'd have to order a book and wait 3+ days for it to arrive otherwise. Because while libraries exist they focus on books in my native language and not English.

by JCattheATM3 days ago|

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> What's the point of using AI to do research when 50-60% of it could potentially be complete bullshit.

Because if you know how to spot the bullshit, or better yet word prompts accurately enough that the answers don't give bullshit, it can be an immense time saver.

by fao_3 days ago|

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> better yet word prompts accurately enough that the answers don't give bullshit

The idea that you can remove the bullshit by simply rephrasing also assumes that the person knows enough to know what is bullshit. This has not been true from what I've seen of people using AI. Besides, if you already know what is bullshit, you wouldn't be using it to learn the subject.

Talking to real experts will win out every single time, both in time cost, and in socialisation. This is one of the many reasons why networking is a skill that is important in business.

by JCattheATM2 days ago|

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> The idea that you can remove the bullshit by simply rephrasing also assumes that the person knows enough to know what is bullshit. This has not been true from what I've seen of people using AI. Besides, if you already know what is bullshit, you wouldn't be using it to learn the subject.

Take coding as an example, if you're a programmer you can spot the bullshit (i.e. made up libraries), and rephrasing can result in entire code being written, which can be an immense time saver.

Other disciplines can do the same in analogous ways.

by bethekidyouwant3 days ago|

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It’s really useful for summarizing extremely long comments.

by CamperBob23 days ago|

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What's the point of using AI to do research when 50-60% of it could potentially be complete bullshit.

You realize that all you have to do to deal with questions like "Marathon Crater" is ask another model, right? You might still get bullshit but it won't be the same bullshit.

by thatjoeoverthr3 days ago|

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I was thinking about a self verification method on this principle, lately. Any specific-enough claim, e.g. „the Marathon crater was discovered by …” can be reformulated as a Jeopardy-style prompt. „This crater was discovered by …” and you can see a failure to match. You need some raw intelligence to break it down though.

by Night_Thastus3 days ago|

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Without checking every answer it gives back to make sure it's factual, you may be ingesting tons of bullshit answers.

In this particular answer model A may get it wrong and model B may get it right, but that can be reversed for another question.

What do you do at that point? Pay to use all of them and find what's common in the answers? That won't work if most of them are wrong, like for this example.

If you're going to have to fact check everything anyways...why bother using them in the first place?

by CamperBob23 days ago|

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If you're going to have to fact check everything anyways...why bother using them in the first place?

"If you're going to have to put gas in the tank, change the oil, and deal with gloves and hearing protection, why bother using a chain saw in the first place?"

Tool use is something humans are good at, but it's rarely trivial to master, and not all humans are equally good at it. There's nothing new under that particular sun.

by Night_Thastus3 days ago|

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The difference is consistency. You can read a manual and know exactly how to oil and refill the tank on a chainsaw. You can inspect the blades to see if they are worn. You can listen to it and hear how it runs. If a part goes bad, you can easily replace it. If it's having troubles, it will be obvious - it will simply stop working - cutting wood more slowly or not at all.

The situation with an LLM is completely different. There's no way to tell that it has a wrong answer - aside from looking for the answer elsewhere which defeats its purpose. It'd be like using a chainsaw all day and not knowing how much wood you cut, or if it just stopped working in the middle of the day.

And even if you KNOW it has a wrong answer (in which case, why are you using it?), there's no clear way to 'fix' it. You can jiggle the prompt around, but that's not consistent or reliable. It may work for that prompt, but that won't help you with any subsequent ones.

by CamperBob23 days ago|

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The thing is, nothing you've said is untrue for any search engine or user-driven web site. Only a reckless moron would paste code they find on Stack Overflow or Github into their project without at least looking it over. Same with code written by LLMs. The difference is, just as the LLM can write unit tests to help you deal with uncertainty, it can also cross-check the output of other LLMs.

You have to be careful when working with powerful tools. These tools are powerful enough to wreck your career as quickly as a chain saw can send you to the ER, so... have fun and be careful.

by skydhash3 days ago|

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The nice thing about SO and Github is that there's little to no reason there for things to not work, at least in the context where you found the code. The steps are getting the context, assuming it's true based on various indicators (mostly reputation) and then continuing on with understanding the snippet.

But with LLMs, every word is a probability factor. Assuming the first paragraph is true has no impact on the rest.