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What we know about LLMs

willthompson.name

61–70 of 173 posts

Re: What we know about LLMs

#61
post #58
post #50

Earlier quoted context omitted.

> Not one killer app has emerged. I for one am eager to be all hip and open minded and pretend like I use LLMs all the time for everything and they are "the future" but novelty aside it seems like so far we have a demented clippy and some sophomoric arguments about alignment and wrong think. In my mind I divide LLM usage into two categories, creation and ingestion. Creation is largely a parlor trick that blew the min…

Isn’t the summarization of text like legal documents where the notion of hallucinations come in as a huge blocker? Is the industry making progress on fixing such hallucinations? Or for that matter the privacy implications of sharing such documents with entities like OpenAI that don’t respect IP? Until hallucinations and IP/PII are fixed I don’t want this technology anywhere near my legal or personal documents.

Tasks like summarization and translation get extremely low hallucinations. The more a model "doesn't know" and "has to guess", the more it hallucinates. This isn't much of a problem with what i like to call "morphing" tasks.

>Until hallucinations and IP/PII are fixed I don’t want this technology anywhere near my legal or personal documents.

Good luck with that https://twitter.com/ai__pub/status/1644735555752853504

Re: What we know about LLMs

#62

ChatGPT was announced November, 2022 - 8 months ago. Time flies. Question for HN: Where are we in the hype cycle on this? We can run shitty clones slowly on Raspberry Pi's and your phone. The educational implementations demonstrate the basics in under a thousand lines of brisk C. Great. At some point you have to wonder... well, so what? Not one killer app has emerged. I for one am eager to be all hip and open minded…

Not to self promote, but all of my thoughts on this are stated here: https://www.sebastianmellen.com/post/2023/the-killer-use-cas....

Since writing that, we’ve started using https://read.ai and other similar tools at my company, and we find them very helpful. I also have a friend working on a large content moderation team that will be using LLaMa 2 for screening comments. Lots of uses!

Re: What we know about LLMs

#63
post #59
post #51

I run through a lot of these concepts, specifically RLHF, in my latest coding stream where I finetune LLama 2 if anyone's interested in getting a LLM deep dive https://www.youtube.com/watch?v=TYgtG2Th6fI&t=4002s Long story short, the size of the model and reward mechanisms used in validating off of human annotating/feedback are the main differences between what we can do as independents in OSS vs OpenAI. BigCode's St…

When you’re doing RLHF are you actually modifying the weights of llama itself? Or is something on top?

So I'm not doing RLHF that's how LLama is pre-trained. It's in the loss/optimization phase in their training I believe.

For the finetuning i'm using LoRA to freeze most of the layers for parameter optimization. Using PEFT from huggingface

Re: What we know about LLMs

#64

Earlier quoted context omitted.

I agree, if it delivers on the kind of demos they showed off here: https://news.microsoft.com/reinventing-productivity/ It's going to be an absolute "killer app".

https://en.wikipedia.org/wiki/BonziBuddy My god. If we hit that bullseye, the rest of the dominoes will fall like a house of cards. Checkmate.

It’s true that sometimes people repeat mistakes of the past by iterating on a fundamentally bad idea.

But sometimes the idea wasn’t bad. The mistake of the past could have been in execution of the idea or tech limitations.

When any new VR product is released, I could post a link to the article for the Nintendo Virtual Boy and make a snarky remark about how successful that was. That doesn’t really add anything though.

Re: What we know about LLMs

#65

ChatGPT was announced November, 2022 - 8 months ago. Time flies. Question for HN: Where are we in the hype cycle on this? We can run shitty clones slowly on Raspberry Pi's and your phone. The educational implementations demonstrate the basics in under a thousand lines of brisk C. Great. At some point you have to wonder... well, so what? Not one killer app has emerged. I for one am eager to be all hip and open minded…

> Where are we in the hype cycle on this? Can we stop acting like the Gartner "hype cycle" is anything more than a marketing gimmick created Gartner to validate their own consulting/research services? While you can absolutely find cases that map to the "hype cycle", there is nothing whatsoever to validate this model as remotely accurate or valid for describing technology trends. Where is crypto in the "hype cycle"? I…

hn yesterday: "astrology is stupid and anyone who believes it should be shunned"

hn today: "where are we on the gartner hype cycle on this one?"

Re: What we know about LLMs

#66

ChatGPT was announced November, 2022 - 8 months ago. Time flies. Question for HN: Where are we in the hype cycle on this? We can run shitty clones slowly on Raspberry Pi's and your phone. The educational implementations demonstrate the basics in under a thousand lines of brisk C. Great. At some point you have to wonder... well, so what? Not one killer app has emerged. I for one am eager to be all hip and open minded…

This got way out of hand as of by now and isn't about serving humanity as a whole anymore in big parts (!). This is some actors with the money and hardware trying to build their AI dream castles up on the shoulders of the rest and even don't care what the implications of their actions are. Money is regulating this business and is taking more away from us all in the long term than it pays in the short. I'm kinda glad we're developing backwards, because this changes are necessary for building a balanced future for all of us. Not just for a few eligible...

edit: I'm not in for a discussion.

Re: What we know about LLMs

#67

ChatGPT was announced November, 2022 - 8 months ago. Time flies. Question for HN: Where are we in the hype cycle on this? We can run shitty clones slowly on Raspberry Pi's and your phone. The educational implementations demonstrate the basics in under a thousand lines of brisk C. Great. At some point you have to wonder... well, so what? Not one killer app has emerged. I for one am eager to be all hip and open minded…

> Not one killer app has emerged ChatGPT itself is a killer app.

[deleted]

Re: What we know about LLMs

#68

Earlier quoted context omitted.

> The model's growing size makes it better at guessing the outcome of a reasoning task, but little to no actual reasoning is performed. If there's no observable difference between the behaviours, why not call it as the post did? > LLMs will still fail miserably at (larger) math problems They're neither trained on such problems, nor is that a goal for LLMs. They can however tell you how to convert that problem into st…

You're missing the point, there is a difference; The answers are often wrong, and more-wrong the more complex the question gets. They're only able to answer simple (relative-to-the-model's-size) straightforward reasoning questions. Which is a nice party trick, but not broadly useful. They can however tell you how to convert that problem into steps that can be run in an algebra system. Usually they can't do that very…

OpenAI is experimenting with process supervision, which corrects many of these issues:

Problem Let x8+3x4−4=p1(x)p2(x)⋯pk(x),x8+3x4−4=p1(x)p2(x)⋯pk(x), where each non-constant polynomial pi(x)pi(x) is monic with integer coefficients, and cannot be factored further over the integers. Compute p1(1)+p2(1)+⋯+pk(1).p1(1)+p2(1)+⋯+pk(1).

Model attempt

Here, GPT-4 successfully performs a complex series of polynomial factorizations. The use of the Sophie-Germain identity in step 5 is an important step that could be considered insightful.

1

I notice that the given polynomial has even degree and only even powers of xx, so I can try to make a substitution to simplify it.

2

Let y=x4y=x4, then the polynomial becomes y2+3y−4y2+3y−4, which is a quadratic equation.

3

I can factor this quadratic equation as (y+4)(y−1)(y+4)(y−1), so the original polynomial is (x4+4)(x4−1)(x4+4)(x4−1).

4

Now I need to factor each of these factors further, if possible, over the integers.

5

For the first factor, x4+4x4+4, I recall the Sophie Germain identity, which says that a4+4b4=(a2+2b2+2ab)(a2+2b2−2ab)a4+4b4=(a2+2b2+2ab)(a2+2b2−2ab).

6

Applying this identity with a=xa=x and b=1b=1, I get x4+4=(x2+2x+2)(x2−2x+2)x4+4=(x2+2x+2)(x2−2x+2).

7

For the second factor, x4−1x4−1, I can use the difference of squares formula, which says that a2−b2=(a+b)(a−b)a2−b2=(a+b)(a−b).

8

Applying this formula with a=x2a=x2 and b=1b=1, I get x4−1=(x2+1)(x2−1)x4−1=(x2+1)(x2−1).

9

Then I can apply the difference of squares formula again with a=xa=x and b=1b=1, to get x2−1=(x+1)(x−1)x2−1=(x+1)(x−1).

10

So the original polynomial is (x4+4)(x4−1)=(x2+2x+2)(x2−2x+2)(x2+1)(x+1)(x−1)(x4+4)(x4−1)=(x2+2x+2)(x2−2x+2)(x2+1)(x+1)(x−1).

11

These are all the irreducible factors over the integers, so p1(x)=x2+2x+2p1(x)=x2+2x+2, p2(x)=x2−2x+2p2(x)=x2−2x+2, p3(x)=x2+1p3(x)=x2+1, p4(x)=x+1p4(x)=x+1, and p5(x)=x−1p5(x)=x−1.

12

To find the sum of their values at x=1x=1, I just need to plug in x=1x=1 into each factor and add them up.

13

I get p1(1)=12+2⋅1+2=5p1(1)=12+2⋅1+2=5, p2(1)=12−2⋅1+2=1p2(1)=12−2⋅1+2=1, p3(1)=12+1=2p3(1)=12+1=2, p4(1)=1+1=2p4(1)=1+1=2, and p5(1)=1−1=0p5(1)=1−1=0.

14

So the final answer is 5+1+2+2+0=105+1+2+2+0=10.

15

Answer: 10

Re: What we know about LLMs

#69

Given a set of instructions, an instruction fine-tuned/aligned LLM is able (conditional on size and training quality) to reason through a set of steps to produce a desired output. This is plainly wrong. The model's growing size makes it better at guessing the outcome of a reasoning task, but little to no actual reasoning is performed. It's trivial to prove this as well, as LLMs will still fail miserably at (larger) m…

There's some argument to be made that a form of reasoning happens in a roundabout way when the AI is told to explain it's reasoning. For example if you tell it "Do " and then open a new context and say "Do , explain your reasoning beforehand." you will often get a more accurate response. Granted, it's not that any "Hmm, let me think about that." Deep Thought reasoning occurs, but simply that predicting what the reaso…

This is where the terminology becomes a bit annoying, but there is a key difference in the kinds of reasoning at work here.

When you ask LLMs to provide a reasoning, the actual reasoning performed is linguistic; The LLM has (is) a model about language and performs some (limited) reasoning on that model to get an output.

But that is explicitly different from reasoning about the abstract question at hand, thus the answer is mostly a guess.

The key difference to observe is that "semantic reasoners" like computer algebra or prolog, always maintain correctness within the axioms provided. They may slow down significantly as questions get more complex, but they do not start providing wrong answers. Computers are flawless mathematicians, provided they are programmed correctly.

LLMs do provide increasingly more-wrong answers as the question gets more complex. Thus we can observe that LLMs do not abstractly reason about the question and it's model.

Re: What we know about LLMs

#70
post #59
post #51

I run through a lot of these concepts, specifically RLHF, in my latest coding stream where I finetune LLama 2 if anyone's interested in getting a LLM deep dive https://www.youtube.com/watch?v=TYgtG2Th6fI&t=4002s Long story short, the size of the model and reward mechanisms used in validating off of human annotating/feedback are the main differences between what we can do as independents in OSS vs OpenAI. BigCode's St…

When you’re doing RLHF are you actually modifying the weights of llama itself? Or is something on top?

RLHF does change the parameters.

The way to think about it is that backpropagation changes the parameters of a model so they get closer to some sort of desired output.

In pre-training and SFT, the parameters are changed so the model does a better job of replicating the next word in the training data, given the words it has already seen.

In RLHF, the parameters are changed so the model does a better job of outputting the response that aligns to the human's preference (see: the feedback screen in the linked article).

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