[jmalicki]

As a side gig, I write novel software that solves problems no existing software does, that existing LLMs have difficulty reproducing, purely for the purpose of existing as LLM training data.

There are journalists being hired to write Atlantic-worthy articles that exist only as LLM training data, because they're getting paid more than the Atlantic would pay them for it.

It's insane.

Yes, they are hiring the experts themselves. To create new knowledge above and beyond what's on the internet. To be locked away as LLM training data.

The largest characteristic of all of this new data is it is targeted at LLM's weak points.

It's not just more data, it's custom tutorials built for what LLMs struggle at.

[palmotea]

> There are journalists being hired to write Atlantic-worthy articles that exist only as LLM training data, because they're getting paid more than the Atlantic would pay them for it.

I kinda doubt that quality-assertion of "Atlantic-worthy." While I have no doubt such articles are written solely as training data, I'd expect their quality to be much less than the real thing, since there's no public to critique them, probably little reputational risk for errors, and no professional ethics to uphold. Even if professional journalists were hired to do the work, I'd expect them to start phoning it in pretty quick, and skimp on fact-checking especially.

[jmalicki]

That's where the review process comes in.

I've found the review process for these things to be far more vicious and demanding than in the real world.

[palmotea]

> That's where the review process comes in.

> I've found the review process for these things to be far more vicious and demanding than in the real world.

1. for your software or for these "Atlantic-worthy articles"?

2. Why would the review process hold up to the standards of a first-rate publication? I'd expect the review process to succumb to the same pressures I'd outlined.

"Someone paid me to do it" will only get you so much motivation. And asking someone to write something whose only destiny is to get fed into the maw of a machine and mangled, seems like a way to eliminate all other sources of motivation.

[jmalicki]

Journalism is not my field, so I can't deeply judge how effective the reviews are.

In software, the reviews for generating things that only exist as training data are far more rigorous than anything I've seen for things going into production at top companies.

I agree with you that it would make sense it would be phoned in. My experience says the opposite.

[istinetz]

> journalists

> professional ethics to uphold

[MattRogish]

I'm not saying they are not trying - I'm saying we're inventing new problems faster than any Lab can:

1) Identify the gaps

2) Determine how to fix them

3) Implement a fix (especially if that fix is: identify and find experts)

4) And judge the result

How do they know [person] is an expert in [some field]? How do they find that person? How many experts are necessary to give the right information? How do we evaluate the results, especially if it's novel?

You can find a lot of people who disagree on many topics, and those turtles go all the way down.

I'm not in disagreement that your work will help reduce hallucinations and improve model performance! It is.

I predict (I hope I'm wrong!) that we're going to hit some asymptote that is not at 0% hallucinations (and I would even put a substantial nonzero probability that "overall" hallucination rate bottoms out at some minimum and then slowly grows because we just can't keep up with the new garbage we throw at it).

[sroussey]

> How do they know [person] is an expert in [some field]? How do they find that person?

You just stumbled upon billion dollar businesses: Mercor, micro1, Scale AI, Surge AI, etc

[jmalicki]

> How do they know [person] is an expert in [some field]? How do they find that person?

They have a PhD from a top school, they are a licensed attorney, they are a licensed physician, a board certified cardiologist, etc.

They are constantly recruiting from these populations with well-paying side gigs.

> 4) And judge the result

That's what they pay the experts for. And to have experts review the other experts with peer review.

> You can find a lot of people who disagree on many topics, and those turtles go all the way down.

Which is why everything has to be well-calibrated and not just a hot take - a well reasoned opinion any expert would find fair.

Noone is really caring about hallucinations on point facts these days though, it is much more about complex reasoning tasks. Can they move the bar on the complexity of software LLMs do on their own? Can they get to a point where LLMs can begin to replace physicians? Financial advisors? Actuaries? etc.

[macleginn]

> Noone is really caring about hallucinations on point facts these days though, it is much more about complex reasoning tasks.

The boundary is pretty thin there though. E.g., Gemini recently told me that a certain papers claims that two frameworks are mathematically equivalent, while the paper shows the opposite, and yesterday Google's AI overview told me that no World Cup matches were scheduled for that day despite their being several of them. The model probably used complex reasoning to arrive at both (incorrect) answers, but superficially they look like basic errors of fact.

[jmalicki]

That is a great example of the kind of thing they're paying people to create as training data.

You write the prompt, and then write rubrics to judge the responses, and you found something the model failed at. Congratulations, you just earned $500, now do it again.

[macleginn]

Not the worst way to make money, but if internet-scale data were not enough to reduce errors to a somewhat tolerable margin, how much data do they hope to collect in this manner?

[jmalicki]

Right now, this is a 10-figure run rate industry.

They are generating a lot of this. Also remember it's not just quantity, it's roughly active learning - they're paying for training data that's at the classification boundary, which is way more valuable.

I have gotten offers for contracts for full time jobs at high rates with AI labs to do this.

Meta has reallocated a lot of their full time SWE staff to do this.

All of this has rapidly accelerated within the last 6 months, who knows far it will go, if someone showed me a Kalshi bet that 10% of the college educated population of the US would be doing this as their primary job by the end of 2027, I wouldn't have the guts to bet against it.

10% of physicians' earnings doing this? Yeah that would totally track.

It doesn't seem like there's a limit. There's a shortage of GPUs and TSMC can only scale up so fast, so the AI labs found something else to spend money on.

[maxnevermind]

That is informative, I was suspecting that is how models improve their performance on some convoluted "non-googlabe" benchmarks like SimpleBench, that is how, they just got the taste of those those questions from publicly available samples and then hired people to generate similar questions and provide answers for them.

I wonder if extracting those static reasoning chains make sense given a Rich Sutton's "The Bitter Lesson" and Geoffrey Hinton's "People should stop training radiologists now.". I guess until participants make money they won't stop, not sure if they do, so far it is more about expectation of profitability as I understand.

[jmalicki]

There is one level that these training data give examples of specific static reasoning chains.

Given exposure to enough reasoning chains, with training data that is designed around adversarial reasoning and teaching models to reason, these types of training data might be key to teaching models to reason beyond what they could gather from static data.

[maxnevermind]

> these types of training data might be key to teaching models to reason beyond what they could gather from static data.

I was under impression that every time LLMs try to be truly novel and they need to assume things in the area where they didn't have enough data points that there were trained on, results are not good, has that changed?

[jmalicki]

If LLMs were already good at it, the AI labs wouldn't be paying this insane amount of money for people to generate training data to teach them.

[giardini]

Ahhhh! the ever-present omniscient "they" of paranoia!

But be careful: they are watching you and they don't want you giving away their secrets!

[ayewo]

1. How did you land the side gig? Mercor or a lessor known brand?

2. What criteria do such vendors typically require?

[jmalicki]

I've done Mercor and other brands - the contracts move around, since the labs want the vendors to know they're just vendors and have to compete with each other. It seemed to be roughly resume and interview similar to getting hired at a senior role at FAANG or adjacent.

[victorbjorklund]

What kind of programs? Can you give an example of the tasks?

[jmalicki]

I cannot give examples.

For the more interesting contracts, I create examples from whole cloth.

[calla]

Hi i would like to hear More about this.Where is the citation?Or can you send more information ? Who is paying

[calla]

Hi so mercor is paying them? What's the prompt? Need more info!

[coldtea]

>There are journalists being hired to write Atlantic-worthy articles that exist only as LLM training data, because they're getting paid more than the Atlantic would pay them for it.

So now part of the training is directly slop - just of the pre-AI variety?

[giardini]

jmalicki says many things, among them being

"As a side gig, I write novel software that solves problems no existing software does,"

and

"Yes, they are hiring the experts themselves. To create new knowledge above and beyond what's on the internet. To be locked away as LLM training data."

More likely you're joking and/or paranoid!8-))

[nomel]

> I write novel software that solves problems no existing software does

This is actually really easy to do if you step out of web/gui/crud and into something where you won't find public code, most ever, because it's trade secret. For example, manufacturing.

[jmalicki]

There is also an endless fountain of things you come across every day and think "oh, wouldn't this complex solution to this low priority problem be cool", but noone ever implements it because it's too complex and the problem is low priority.

Anyone writing software for long enough has a long list of these things in the back of their head that are great fodder for LLM training data.

[knollimar]

I make a trello board to direct spare tokens at when I'm bored now!

[jmalicki]

I wish our actual world wasn't an implausible scifi novel!