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Today's Large Language Models Are Essentially BS Machines

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Re: Today's Large Language Models Are Essentially BS Machines

#21

Post by jjice »

httpz wrote: post #16

To be honest, I hated writing essays in English classes because I felt like I'm forced to write BS to fill up the space when my argument can be summed up in several bullet points. Since I'm not a student anymore, I can just give ChatGPT a few bullet points and ask it to write a paragraph for me. As an engineer who doesn't like writing "fluff", it's great I can now outsource the BS part of writing.

As an engineer I'd hope you wouldn't have to write fluff. Brevity (while retaining full content) should be praised.

I'm interested what parts of your job require the fluff? Is it communication with non engineering teams?

Re: Today's Large Language Models Are Essentially BS Machines

#22

Post by borissk »

coliveira wrote: post #12

I think that an AI-powered world will create a population that doesn't know how to distinguish truth from lies. People already believe that AI has some powerful hidden knowledge that they need to use, even when the AI model is spilling garbage. In the future, they will also be incapable to separate what AI models tell from reality.

People already can hardly distinguish truth from lie. One Donald Trump lies constantly. Brexit referendum in UK was driven by a ton of lies, many people still believe them.

Re: Today's Large Language Models Are Essentially BS Machines

#24

Post by haimez »

dekhn wrote: post #13

During the big GPT-4 news cycle I think a bunch of folks posted claims that were outrageously good- "language model passes medical exams better than humans", etc. When I looked into them, in nearly all cases, the claims were boosted far beyond the reality. And the reality seemed much more consistent with a fairly banal interpretation: LLMs produce realistic looking text but have no real ability to distinguish truth f…

LLM’s are spitting out responses based on their inputs. It is (or was) shockingly effective, but there is no generalized math processing going on. That’s not what LLM’s are, that’s not how they work.

Re: Today's Large Language Models Are Essentially BS Machines

#25

Post by pclmulqdq »

jjice wrote: post #21
httpz wrote: post #16

To be honest, I hated writing essays in English classes because I felt like I'm forced to write BS to fill up the space when my argument can be summed up in several bullet points. Since I'm not a student anymore, I can just give ChatGPT a few bullet points and ask it to write a paragraph for me. As an engineer who doesn't like writing "fluff", it's great I can now outsource the BS part of writing.

As an engineer I'd hope you wouldn't have to write fluff. Brevity (while retaining full content) should be praised. I'm interested what parts of your job require the fluff? Is it communication with non engineering teams?

A lot of people seem to think that details that other people think are relevant are actually "fluff."

Re: Today's Large Language Models Are Essentially BS Machines

#26

Post by DennisP »

httpz wrote: post #16

To be honest, I hated writing essays in English classes because I felt like I'm forced to write BS to fill up the space when my argument can be summed up in several bullet points. Since I'm not a student anymore, I can just give ChatGPT a few bullet points and ask it to write a paragraph for me. As an engineer who doesn't like writing "fluff", it's great I can now outsource the BS part of writing.

Yep it's great for work emails. Incoming too, since they can summarize a long email into bullet points.

The future is people typing bullet points, expanding into polished prose for transmission, and compressing down to bullet points on the other end.

Re: Today's Large Language Models Are Essentially BS Machines

#27

Post by btown »

Even if you take the headline at face value (and IMO it's rather unfair)... the incredible saving grace of LLMs is that you have a plurality of BS machines, with different flavors of BS, whose outputs can be wired together.

Sure, the first-order output of today's generalist LLMs outputting one token at a time do seem to meet meet diminishing returns on factuality at approximately the level of a college freshman pulling an all-nighter. Not a great standard, that. But if you took an entire class of those tired freshmen, gave their outputs to an independent group of tired freshmen unfamiliar with the material, and told the second group to identify, in a structured manner, commonalities and discrepancies and topics they'd look up in an encyclopedia and things they'd like to escalate to a human expert on, and so on... all of a sudden, you can start to build structured knowledge about the topic, and an understanding of what is and isn't likely to be a hallucination.

One might argue that the right kind of model architecture and RLHF could bake this into the LLM itself - but you don't need to wait for that research to be brought into production to create a self-correcting system-of-systems today.

Re: Today's Large Language Models Are Essentially BS Machines

#30

Post by dekhn »

haimez wrote: post #24
dekhn wrote: post #13

During the big GPT-4 news cycle I think a bunch of folks posted claims that were outrageously good- "language model passes medical exams better than humans", etc. When I looked into them, in nearly all cases, the claims were boosted far beyond the reality. And the reality seemed much more consistent with a fairly banal interpretation: LLMs produce realistic looking text but have no real ability to distinguish truth f…

LLM’s are spitting out responses based on their inputs. It is (or was) shockingly effective, but there is no generalized math processing going on. That’s not what LLM’s are, that’s not how they work.

And yet, trained on a large corpora of correct math statements, they produce responses that are more often right than wrong (I am taking this for true- it might not be)- which simply raises more questions about the nature of math.
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