Library asks users to verify that books actually exist before making a loan request because AI invents book titles

Ultimately LLMs are just a tool, and like other tools they cannot be applied to every task. You wouldn't blame a hammer for being bad at installing screws, would you?

LLMs as a tool are designed for generating tokens. They build a statistical model through extensive training, and can apply this for text completion and transformation tasks. If you use them for this purpose, they hardly ever hallucinate. This makes them exceptional for data manipulation, translation, editing, error detection, and so on.

What LLMs are not however is a database of information. If you ask them for specific facts, especially for recent or niche topics, a hallucination is very likely. Getting the correct answer is a statistical probability, and one based on a number of factors probably unknown to the asker. If somebody takes information from an LLM at face value, they are not using it responsibly. Essentially all LLM providers includes warnings on this topic before they may be used, but maybe they should be more aggressively plastered.

This doesn't mean that LLMs are useless for fact finding. They still often provide an excellent introduction to a topic. They also allow you to ask the questions that you don't really know how to ask yet; you can't Google a term you don't know the name for yet. But their limitations need to be kept in mind, and they should be used more as a jumping off point than the final say on any topic.

Twenty years ago we needed to learn how to effectively use search engines. Which keywords to use, the order to place them in, and the search operators to use. LLMs are really no different. Using them effectively requires practice, and their features and limitations are still being worked out.

When you asked Copilot for a list of books, those titles may or may not have existed. If it's a common enough topic then there's a good chance that information was actually incorporated into its model, but you'd have to double check to be sure. When you asked it again however if they were real, you primed it to respond in a certain way. It was generating the most likely tokens based on your follow-up question, which is why it went on to claim they weren't real. This is a hallucination. In reality, it had no idea if they were real or not. Such is their nature.

Hammers are useful, and so are LLMs. They are more complex tools though, and require more practice to get right. But even in their early form, they're providing a lot of value when used correctly, and are definitely worth keeping around in your toolbox.

How LLM AI (non AGI) is being used right now is a mess. Anything that requires things to be true shouldn't be using them, and yet that's exactly how they're being marketed and purchased: customer service to provide accurate company info; replacement for data acquisition and report generation staff; news articles; self driving cars.... We're not there yet and people are being idiots thinking we are.

Not quite there. We can be. This library example is prime: make use of the friendliness of language models, but have a second layer to cross reference fact check, and we're mostly there n

People keep trying to use current LLMs and AI portals as if the technology is in its final mature form. It isn't yet.

Right now, an LLM is just a piece of sophisticated software that can assemble words into coherent order. So coherent that it can do it in reverse and form pretty good guesses as to what a human "means" when that human gives it text input. The LLM can then formulate a coherent response.

That's not the same thing as the LLM having actual, fully accurate, detailed, trustworthy ability to comb through the bulk of human knowledge (aka, the Internet) and "know" what bits and pieces are relevant.

You ask a physics professor about physics, and she'll have relevant things to say. When you ask specific questions, and continue dialing down by degrees of detail, she'll still be able to stay relevant and on-point with her answers. She knows physics.

The LLM just knows how to sound like a human. That's not the same thing as having a human brain to use on the information (databases).

That physics professor, should she be unskilled or otherwise lack aptitude with human communication, could tell an LLM what to say and the LLM could come up with ways for her to say it. And with some of the other AI technologies, even proceed to verbally say it in almost any voice desired. So she could "hide" behind the tech and use it to communicate, and then it would pretty much do what most people seem to think AI/LLM tech can do without the human in the loop.

But without her, the tech is still all form and surface-only function. The form, that surface flash, is the technological advancement. The form is very impressive. A problem that was thought to be unsolvable has been solved, that of enabling a computer to use language on a human level. But the technology only sounds like Spock or Data; it isn't Spock or Data yet, much less the Enterprise's library computer.

Yet.

Patience. If people would RTFM more often, and be aware of limitations and restrictions, the rest of us wouldn't have to put up with this parade of "I tried to get new tech to do something new tech doesn't do yet and it couldn't; new tech suxxxxxx!!!!" articles that continue to roll out.

Trying to play gotcha when the tech currently isn't intended to get that thing yet.