I generally will respond to stuff like this with "people do this, too", but this result given their specific examples is genuinely surprising to me, and doesn't match at all my experience with using LLMs in practice, where it does frequently ignore irrelevant data in providing a helpful response.

I do think that people think far too much about 'happy path' deployments of AI when there are so many ways it can go wrong with even badly written prompts, let alone intentionally adversarial ones.

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achierius 4 days ago | parent | next [-]

> I generally will respond to stuff like this with "people do this, too"

But why? You're making the assumption that everyone using these things is trying to replace "average human". If you're just trying to solve an engineering problem, then "humans do this too" is not very helpful -- e.g. humans leak secrets all the time, but it would be quite strange to point that out in the comments on a paper outlining a new Specter attack. And if I were trying to use "average human" to solve such a problem, I would certainly have safeguards in place, using systems that we've developed and, over hundreds of years, shown to be effective.

	▲	saurik 3 days ago \| parent \| next [-]
		Well, if you are going to try to use an LLM--something that is a giant black box that has no hope any time soon of being proven anywhere near as reliable as a CPU, and which has been trained explicitly on input data that makes it remarkably similar with respect to its limitations to a human--then you need to get used to using it to replace the "average human" and start doing everything you can to convince yourself it is a human so that you don't forget to add all of those safeguards we have shown to be effective.
	▲	empath75 3 days ago \| parent \| prev [-]
		One can talk about LLMs in contexts that aren't about engineering, and are instead about topics like: "Do LLMs think" or "Are LLMs intelligent". People _frequently_ point to some failure mode of LLMs as dispositive proof that LLMs are incapable of thinking or aren't intelligent, in which case it is relevant that humans, which are universally agreed to be intelligent, frequently make similar mistakes.

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JambalayaJimbo 4 days ago | parent | prev | next [-]

Autonomous systems are advantageous to humans in that they can be scaled to much greater degrees. We must naturally ensure that these systems do not make the same mistakes humans do.

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Ekaros 3 days ago | parent | prev [-]

When I think lot of use cases LLMs are planned for. I think not happy paths are critical. There is not insignificant number of people who would ramble about other things to customer support person if given opportunity. Or lack capability to only state needed and not add extra context.

There might be happy path when you isolated to one or a few things. But not in general use cases...