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LLM Inevitabilism

(tomrenner.com)
1611 points SwoopsFromAbove | 3 comments | | HN request time: 0.035s | source
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lsy ◴[] No.44568114[source]
I think two things can be true simultaneously:

1. LLMs are a new technology and it's hard to put the genie back in the bottle with that. It's difficult to imagine a future where they don't continue to exist in some form, with all the timesaving benefits and social issues that come with them.

2. Almost three years in, companies investing in LLMs have not yet discovered a business model that justifies the massive expenditure of training and hosting them, the majority of consumer usage is at the free tier, the industry is seeing the first signs of pulling back investments, and model capabilities are plateauing at a level where most people agree that the output is trite and unpleasant to consume.

There are many technologies that have seemed inevitable and seen retreats under the lack of commensurate business return (the supersonic jetliner), and several that seemed poised to displace both old tech and labor but have settled into specific use cases (the microwave oven). Given the lack of a sufficiently profitable business model, it feels as likely as not that LLMs settle somewhere a little less remarkable, and hopefully less annoying, than today's almost universally disliked attempts to cram it everywhere.

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brokencode ◴[] No.44572627[source]
> “most people agree that the output is trite and unpleasant to consume”

That is a such a wild claim. People like the output of LLMs so much that ChatGPT is the fastest growing app ever. It and other AI apps like Perplexity are now beginning to challenge Google’s search dominance.

Sure, probably not a lot of people would go out and buy a novel or collection of poetry written by ChatGPT. But that doesn’t mean the output is unpleasant to consume. It pretty undeniably produces clear and readable summaries and explanations.

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underdeserver ◴[] No.44573204[source]
> That is a such a wild claim. People like the output of LLMs so much that ChatGPT is the fastest growing app ever.

The people using ChatGPT like its output enough when they're the ones reading it.

The people reading ChatGPT output that other people asked for generally don't like it. Especially if it's not disclosed up front.

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ohyes ◴[] No.44573861{3}[source]
Had someone put up a project plan for something that was not disclosed as LLM assisted output.

While technically correct it came to the wrong conclusions about the best path forward and inevitably hamstrung the project.

I only discovered this later when attempting to fix the mess and having my own chat with an LLM and getting mysteriously similar responses.

The problem was that the assumptions made when asking the LLM were incorrect.

LLMs do not think independently and do not have the ability to challenge your assumptions or think laterally. (yet, possibly ever, one that does may be a different thing).

Unfortunately, this still makes them as good as or better than a very large portion of the population.

I get pissed off not because of the new technology or the use of the LLM, but the lack of understanding of the technology and the laziness with which many choose to deliver the results of these services.

I am more often mad at the person for not doing their job than I am at the use of a model, the model merely makes it easier to hide the lack of competence.

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1. thewebguyd ◴[] No.44574420{4}[source]
> do not have the ability to challenge your assumptions or think laterally.

Particularly on the challenging your assumptions part is where I think LLMs fail currently, though I won't pretend to know enough about how to even resolve that; but right now, I can put whatever nonsense I want into ChatGPT and it will happily go along telling me what a great idea that is. Even on the remote chance it does hint that I'm wrong, you can just prompt it into submission.

None of the for-profit AI companies are going to start letting their models tell users they're wrong out of fear of losing users (people generally don't like to be held accountable) but ironically I think it's critically important that LLMs start doing exactly that. But like you said, the LLM can't think so how can it determine what's incorrect or not, let alone if something is a bad idea or not.

Interesting problem space, for sure, but unleashing these tools to the masses with their current capabilities I think has done, and is going to continue to do more harm than good.

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2. DrewADesign ◴[] No.44575562[source]
I'm no expert, but the most frequent recommendations I hear to address this are:

a) tell it that it's wrong and to give you the correct information.

b) use some magical incantation system prompt that will produce a more critical interlocutor.

The first requires knowing enough about the topic to know the chatbot is full of shit, which dramatically limits the utility of an information retrieval tool. The second assumes that the magical incantation correctly and completely does what you think it does, which is not even close to guaranteed. Both assume it even has the correct information and is capable of communicating it to you. While attempting to use various models to help modify code written in a less-popular language with a poorly-documented API, I learned how much time that can waste the hard way.

If your use case is trivial, or you're using it as a sounding board with a topic you're familiar with as you might with, say, a dunning-kruger-prone intern, then great. I haven't found a situation in which I find either of those use cases compelling.

3. myrryr ◴[] No.44575564[source]
This is why once you are using to using them, you start asking them for there the plan goes wrong. They won't tell you off the bat, whuch can be frustrating, but they are really good at challenging your assumptions, if you ask them to do so.

They are good at telling you what else you should be asking, if you ask them to do so.

People don't use the tools effectively and then think that the tool can't be used effectively...

Which isn't true, you just have to know how the tool acts.