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Comment by brey | Hacker News Reader

The next sentence after your quoted section:

“Even then, AI output is never treated as an authoritative source. Everything must be verified.”

Any verification process thorough enough to catch all LLM fabrications would take more work than simply not using the LLM in the first place. If anything verifying what an LLM wrote is substantially more difficult than just reading the material it's "summarising", because you need to fully read and comprehend the material and then also keep in mind what the LLM generated to contrast and at that point what the fuck are you even doing?

I believe this policy can never result in a positive outcome. The policy implicitly suggests that verification means taking shortcuts and letting fabrications slip through in the name of "efficiency", with the follow-up sentence existing solely so that Ars won't take accountability for enabling such a policy but instead place the blame entirely on the reporters it told to take shortcuts.

> I believe this policy can never result in a positive outcome.

I get where you're coming from (I'm learning more and more over time that every sentence or line of code I "trust" an AI with, will eventually come back to bite me), but this is too absolutist. Really, no positive result, ever, in any context? We need more nuanced understanding of this technology than "always good" or "always bad."

I didn't say in any context. I'm specifically talking about this policy on journalistic research.

If you need accuracy, an LLM is not the tool for that use case. LLMs are for when you need plausibility. There are real use cases for that, but journalism is not one of them.

> Any verification process thorough enough to catch all LLM fabrications would take more work than simply not using the LLM in the first place

Sometimes you have a weak hunch that may take hours to validate. Putting an LLM to doing the preliminary investigation on that can be fruitful. Particularly if, as if often the case, you don't have a weak hunch, but a small basket of them.

You can prompt LLMs to scan thousands of documents to generate text validating your hunches. In some cases those validated hunches may even be correct.

It's easy to get an LLM to make any argument you like based on whatever data is available. Those arguments are going to be trivially bad if that data is bad.

Disagree. If I’m I’m a reporter and I’m trawling though a mass data dump - say the Epstein files or Wilileaks or statistics on environmental spills or something, using AI to pull out potential patterns in the data, or find specific references can be useful. Obviously you go and then check the particular citations. This will still save a lot of time.

The LLM can find material that it would be hard or time-consuming for you to do.

You still need to verify it, but "find the right things to read in the first place" is often a time intensive process in itself.

(You might, at that point, argue that "what if LLM fails to find a key article/paper/whatever", which I think is both a reasonable worry, and an unreasonable standard to apply. "What if your google search doesn't return it" is an obvious counterpoint, and I don't think you can make a reasonable argument that you journalists should be forced to cross-compare SERPs from Google/Bing/DuckDuckGo/AltaVista or whatever.)

I believe what their point is is that if you give people a "extract-needle-from-haystack" machine and then tell them they have to manually find where in the haystack the needle was, it defeats the purpose of having the machine.

With that said, a good RAG solution would come with metadata to point to where it was sourced from.

> I believe what their point is is that if you give people a "extract-needle-from-haystack" machine and then tell them they have to manually find where in the haystack the needle was, it defeats the purpose of having the machine.

We've got to be careful to not let the perfect be the enemy of the good.

I'm not an LLM enthusiast, but I think you have actually compare it against what the alternative would really be. If you give the journalist a haystack but insufficient time to manually search it properly, they're going to have to take some shortcut. And using an LLM to sort through it and verifying it actually found a needle probably better than randomly sampling documents at random or searching for keywords.

I don't want to come off as an AI-maximalist or whatever, but, I mean, at some point, skill issue, right?

You can use Google to find you results reinforcing your belief that the earth is flat too; but we don't condemn Google as a helpful tool during research.

If you trust whatever the LLM spits out unconditionally, that's sorta on you. But they _can_ be helpful when treated as research assistants, not as oracles.

when you use the extract-needle-from-haystack machine, verify that it actually extracted a needle.

that's much easier than manually extracting the needle yourself

Another interpretation is if you have multiple haystacks, and the machine tells you which haystack likely has a needle in it. You still need to extract the needle yourself,

This is a bogus analogy leaidng to a bogus conclusion.

If something points to the needle in the haytack (saying "this haystack has a needle positioned eighteen centimeters from the top and three left of center"), it's much easier to verify that indeed there is a needle there than it would be to find that needle in the first place.

If an LLM spits out a claim that something happened (citing a certain article), it's less work to read the article and verify the claim than it would be to DISCOVER the article in the first place.

In other words, LLMs can be a time-saving search engine, and the idea that it's just as much work to find+verify information as it is to have the LLM find it and then you verify it is hokum.

It's more using LLMs like a metal detector, rather than digging through the entire beach by yourself.

You still need to check the junk you dig up using the metal detector.

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