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Comment by bensyverson | Hacker News Reader

The article asserts that the quality of human knowledge work was easier to judge based on proxy measures such as typos and errors, and that the lack of such "tells" in AI poses a problem.

I don't know if I agree with either assertion… I've seen plenty of human-generated knowledge work that was factually correct, well-formatted, and extremely low quality on a conceptual level.

And AI signatures are now easy for people to recognize. In fact, these turns of phrase aren't just recognizable—they're unmistakable. <-- See what I did there?

Having worked with corporate clients for 10 years, I don't view the pre-LLM era as a golden age of high-quality knowledge work. There was a lot of junk that I would also classify as a "working simulacrum of knowledge work."

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For me the issue is the lack of human explanation for mistakes. With a person, low quality comes from a source. Sometimes the source is lack of knowledge, sometimes time pressure, sometimes selfish goals.

Most importantly, those sources of errors tend to be consistent. I can trust a certain intern to be careful but ignorant, or my senior colleague with a newborn daughter to be a well of knowledge who sometimes misses obvious things due to lack of sleep.

With AI it's anyone's guess. They implement a paper in code flawlessly and make freshman level mistakes in the same run. so you have to engage in the non intuitive task of reviewing assuming total incompetence, for a machine that shows extreme competence. Sometimes.

It's not that pre-LLM era was a "golden age of quality", far form it. It's that LLMs have removed yet another tell-tale of rushed bullshit jobs.

Have they though?

Absolutely. Our heuristics for judging human output are useless with LLMs. We can either trust it blindly, or tediously pick over every word (guess which one people do). I've watched this cause havoc over and over at my job (I work with many different teams, one at a time).

AI signatures don't mean low quality, they just mean AI. And humans do use them (I have always used the common AI signatures). And yes, humans produce good-looking garbage, but much more commonly they produce bad-looking garbage. This is all tangential to the point.

> Our heuristics for judging human output are useless with LLMs.

We used to call the negative side of these heuristics "code smells", but I see no one has used that term yet in these comments. Code smells are what the post is referring to, what LLMs get rid of.

For example, science articles written in Word vs. Latex helped filter out total cranks.

It was and still is a negative filter, not a positive one. Meaning it is easy to reject work because there typos and basic factual errors, absence of them is not a good measure of quality. Typically such checks is the first pass not the only criteria.

It is valuable to have this, because it the work passes the first check then it easier to identify the actual problems. Same reason we have code quality, lint style fixed before reasoning with the actual logic being written.

Ironic, you've got some typos but make a good point :)

You are right :) [2]

Perhaps it also conveys different type of meaning by having them in this context.

Errors [1] in community discussion threads like this are positive signals that I am human not a bot. A couple of decades ago, I would be unhappy with myself for it, today accent and idiosyncratic writing are perhaps signals[3] that you are human.

[1] i.e. not proof reading for them, not introducing them deliberately.

[2] I can only see one typographical error (it->if) and many grammar errors, did I miss something ?

[3] Not definitive and not as a personal signature, as it can be easily faked/replicated, but the variations at scale is for now not seen in models. Today's model instances do not get unique personas, accents, idiosyncrasies in writing that would make them unique.

> And AI signatures are now easy for people to recognize. In fact, these turns of phrase aren't just recognizable—they're unmistakable. <-- See what I did there?

You might spot these very obvious constructs and still miss 99% of AI generated text because it has no tells. Yet you don’t know that 99% was generated, and since you spot 100% of the pattern you outlined you think no AI generated text makes it past you.

I’m also not sure I agree with the assertion that LLMs will produce a high quality (looking) report with correct time frames, lack of typos, and good looking figures. I’m just as willing to disregard human or LLM reports with obvious tells. An LLM or a person can produce work that’s shoddy or error filled. It may be getting harder to differentiate between a good or bad report, but that helps to shift the burden more onto the evaluator.

This is especially true if we start to see more of a split in usage between LLMs based on cost. High quality frontier models might produce better work at a higher cost, but there is also economic cost pressure from the bottom. And just like with human consultants or employees, you’ll pay more for higher quality work.

I’m not quite sure what I’m trying to argue here. But the idea that an LLM won’t produce a low quality report just seemed silly to me.

You’ve missed the point of original article about the proxy for quality disappearing. LLMs are trained adversarially, if that’s a word. They are trained to not have any “tells”.

Working in a team isn’t adversarial, if i’m reviewing my colleague’s PR they are not trying to skirt around a feature, or cheat on tests.

I can tell when a human PR needs more in depth reviewing because small things may be out of place, a mutex that may not be needed, etc. I can ask them about it and their response will tell me whether they know what they are on about, or whether they need help in this area.

I’ve had LLM PRs be defended by their creator until proven to be a pile of bullshit, unfortunately only deep analysis gets you there

> I don't know if I agree with either assertion…

Yes, I don't think this matters. Much of "knowledge work" was always a proxy for something else.

High quality in terms of typos and errors is mainly a signal of respect in a similar way to wearing ironed white shirts with neck-ties. "Walls of text" that no one is expected to read in depth. Basically a symbolic demonstration of sacrifice and subservience (or something). LLMs remove this mode of signalling.

If quality of content wasn't examined before, it was probably never particularly important.

Yes. I think the main warning here is that it is an added risk. A little glitch here and there until something breaks.

> I don't know if I agree with either assertion… I've seen plenty of human-generated knowledge work that was factually correct, well-formatted, and extremely low quality on a conceptual level.

Putting a high level of polish on bad ideas is basically the grifter playbook. Throughout the business world you will find workers and entire businesses who get their success by dressing up poor ideas and bad products with all of the polish and trimmings associated with high quality work.

The goal of automation is to automate consistently perfect competence, not human failures.

You wouldn't use a calculator that is as good as a human and makes mistakes as often.

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