My guess for the reason behind this is that LLMs have more facts memorized, and thus can make more reasonable and well-researched sounding answers. If you ask an LLM vs a Human "Is a stack in computer science a) a data structure that is first in first out or b) a data structure that is first in last out" the LLM can say stuff resembling "Based on Dijkstra's algorithm proof given in 1943 and the nature of Turing complete languages being traditionally a top-down oriented system, a stack is ..." while a human is just going to say "It's B because that's what a stack is".

Welp, we're boned.

Which incentivized human persuaders? Are we talking about top salespeople and litigators, or are we talking about average college freshmen?

It says they recruited participants from the US through Prolific and paid them £10.12 per hour, so probably more like the latter.

Basically the same finding as the controversial Zurich paper on using LLMs to change opinions in the "change my view" subreddit

We're fast approaching the point that any value someone can provide through a digital interface could be better done by a model. What do we use digital interfaces for? Practically everything.

Oh well not being a plumber, electrician, or farmer... but our society's current productivity, technology, automation reduced our need for 80% of the population needing to be farmers to now 1.3% in the US. Can you imagine what the equivalent of 1 billion digital engineers unlocks in understanding and implementing robotics?

A clear case where LLMs exceed humans is in identifying solutions to disparate shallow constraints involving what would normally require very wide searches of more knowledge than any of us will ever have.

A simple case I have found, is looking for existing or creating new terms. If I have a series of concepts, which I have names for which have a nice linguistic pattern to emphasize their close relationship, except for one. I can describe the regularly named concepts, then ask for suggestions for the remaining concept.

The LLM pulls from virtually every topic with domain terminology, repurposable languages (Greek, Roman), words from fiction, all the way to creative construction of new words, tenses, etc to come up with great proposals in seconds.

I could imagine that crafting persuasive wording would be a similar challenge. Choosing the right words, right phrasing, etc. to carry as much positive connotation, implication of solidity, avoiding anything sounding challenging or controlling, etc. from all of human language and its huge space of emotional constraints and composites.

Very shallow but very wide reasoning/searching/balancing done in very little time.

And with an ability to avoid giving any unnecessary purchase for disagreement, being informed of all the myriad of typical and idiosyncratic ways people get hung up on failed persuasions. Whether in general or specific topic related.

LLM generated writing can be stereotypical.

But the more constraints put on requested material, the more their ability to construct really very original high quality, or even cleverly unique, prose in real time shines.

It's not programmers who should be scared about getting replaced by AI. It's obviously sales people, who should ;)

Based on the data in table 3, I would attribute most of the difference to length of advice. LLMs average word count (29.4) is more than double human word count (13.25). Most other measures do not have a significant ratio. "Difficult word count" would be the only other with a ratio higher than 2, but that is inherited from total word count.

I think it would be difficult to truly convince me to answer differently in a test with 14 words where 30 would have enough space to actually convey an argument.

I would be very interested to see the test rerun while limiting LLM response length or encouraging long responses from humans.

Each of us could benefit from a respective loyal model of our own, critiquing and marking up any persuasive material from others.

It is CRITICAL that we be realistic about what fulfills the optimization objectives in the models that we train. I think there's been a significant unwillingness that objectives like "human preference" (RLHF, DPO, etc) not only help models become more accurate and sound more natural in speech, BUT ALSO optimize the models to be deceptive and convincing when they are wrong. It's easy to see, because you know what's more preferential than a lie? A lie that you don't know is a lie. You (may) prefer the truth, but if you cannot differentiate the truth from a lie you'll preference based on some other criteria. We all know that lies frequently win out here. If you doubt this, just turn on the news or talk to someone that belongs to the opposite political party of yourself.

This creates a very poorly designed tool! A good tool should fail as loudly as possible, in that it alerts the user of the failure and does its best to specify the conditions that led to this. This isn't always possible, but if you look at physical engineers you'll see that this is where they spend a significant portion of their time. Even in software I'd argue we do a lot here, but also that it is easy to brush off (we all love those compiler messages... right?). Clearly right now LLMs are in a state where we don't know how to make their failures more visible, and honestly, that is okay. But what is not okay is to pretend that this is not current reality and pretend that there are no dangers or consequences that this presents. We dismiss this because we catch some obvious errors and over-generalize the error quality, but that just means we suffer from Murray Gell-Mann Amnesia. It's REALLY hard to measure what you don't know. Importantly, we can't even begin to resolve these issues and build the tools we want (the ones we pretend these are!) if we ignore the reality of what we have. You cannot make things better if you are unwilling to recognize their limitations.

Everyone here is an engineer, researcher, or builder. This framework of thinking should be natural to us! We should also be able to understand that there's a huge difference between critiques and limitations and dismissing things. I'm an AI critic, but also very optimistic. I'm a researcher and spending my life working on this topic. It'd be insane to do such a thing if I thought it was a fruitless or evil effort. But it would be equally insane to pursue a topic with pure optimism. If I were to blind myself to limits and paint everything as a trivial to solve problem, I'd never be able to solve any of those problems. Ignoring or dismissing technical issues and limitations is the domain of the MBA managers, not engineers.

Sam Altman must be literally vibrating at the thoughts of tacking on ads at the end of a "persuasive" interaction about whatever. "... and remember to try new Oreo-flavored Pringles and tell them Gippity sent you with this 20% off code, because we are best friends and we can trust each other!"

I can't wait til I have to argue with my manager because I said one thing and the LLM said another thing.

Politicians everywhere, remember this for your next campaign.

I guess our brightest minds will soon use them in advertisements, then.