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Liquid Foundation Models: Our First Series of Generative AI Models

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Re: Liquid Foundation Models: Our First Series of Generative AI Models

#61

Post by space_fountain »

jrflowers wrote: post #51
alach11 wrote: post #46

Earlier quoted context omitted.

All it speaks to is that tokenization is weird and introduces artifacts to LLM performance. Counting letters is a trivial task when you're staring at words on a screen. It's much harder when you're perceiving vectors based on parts of words. The fact that LLMs find certain things easier/harder than humans is completely unsurprising, and there are much more interesting benchmarks to use to compare one LLM to another.

This is a good point. While LLMs being incapable of reliably doing a simple task that’s been doable by computers since the punch card days is an important consideration for anyone that might be thinking about using them for anything other than as a toy , this fact is uninteresting because of Reasons

LLMs can clearly solve problems that computers up to now couldn't. They can't solve all problems and this should definitely be a cautionary note to anyone who wants to use them as an artificial general intelligence, but this take seems no different to someone looking at a punchcard computer and going, it can't even recognize typos or categorize images, what good is this? We've already had human computers who can do everything these can do, and can recognize images and notice typos

Re: Liquid Foundation Models: Our First Series of Generative AI Models

#62

Post by Bilal_io »

k__ wrote: post #58
GaggiX wrote: post #40

Earlier quoted context omitted.

Yes, I agree, for these small models it's wasted potential to be closed source, they can only be used effectively if they are open. EDIT: HN is rate-limiting me so I will reply here: In my opinion 1B and 3B truly shine on edge devices, if not than it's not worth the effort, you can have much better models for already dirt cheap using an API.

While I'm all for open models; why can't the small models not be used effectively? Wouldn't they lower the costs compared to big models drastically?

I think what the parent means is that small models are more useful locally on mobile, IoT devices etc. so it defeats the purpose to have to call an API.

Re: Liquid Foundation Models: Our First Series of Generative AI Models

#63

Post by echelon »

k__ wrote: post #58
GaggiX wrote: post #40

Earlier quoted context omitted.

Yes, I agree, for these small models it's wasted potential to be closed source, they can only be used effectively if they are open. EDIT: HN is rate-limiting me so I will reply here: In my opinion 1B and 3B truly shine on edge devices, if not than it's not worth the effort, you can have much better models for already dirt cheap using an API.

While I'm all for open models; why can't the small models not be used effectively? Wouldn't they lower the costs compared to big models drastically?

These aren't the "small" models I'm thinking of.

I want an LLM, STT, or TTS model to run efficiently on a Raspberry Pi with no GPU and no network.

There is huge opportunity for LLM-based toys, tools, sensors, and the like. But they need to work sans internet.

Re: Liquid Foundation Models: Our First Series of Generative AI Models

#65

Post by sfink »

moralestapia wrote: post #35
alach11 wrote: post #33

Earlier quoted context omitted.

This is one of the least interesting questions to ask LLMs. I wish it wasn't so popular.

And yet ... all they have to do is answer it properly.

It did! How many R's do you think are in "strawberry"? I get 3, and it got 3. I'm with the LLM.

...oh, you disagree with its intermediate reasoning? You think it should follow a series of logical steps that are each individually correct, rather than pulling wildly incorrect intermediate steps out of its butt and synthesizing them together in the end into a coherent answer that happens to be correct?

But that's what an LLM is! Why complain about an LLM being an (unusually transparent) LLM?

I think this example serves as a beautiful illustration of how LLMs work, and are supposed to work—even if the correspondence is inexact between (1) incorrect and irrelevant-to-us intermediate steps and (2) internal matrix multiplications that represent incorrect or invalid logic. The correspondence is inexact and probably mostly figurative, but it's still a great example of how internal nonsense can still lead to externally "correct" answers. ("Nonsense" is underselling the complex and highly sophisticated internal state that leads to "correct" answers a stunningly high percentage of the time; I'm just trying to distinguish it from formal logic or the type of reasoning that we think we do and sometimes actually do. And would do, in this example problem.)

Re: Liquid Foundation Models: Our First Series of Generative AI Models

#67

Post by bondarchuk »

NoCoooode wrote: post #39
alach11 wrote: post #33

Earlier quoted context omitted.

This is one of the least interesting questions to ask LLMs. I wish it wasn't so popular.

That they can't do this sort of simple question speaks volumes to the entire approach. I don't think generative AI will ever be able to reach AGI, and most people selling LLM today pretend it is AGI

For all I care we will have superhuman AGI that still can't count the Rs in strawberry. Some humans are dyslexic and all are subject to weird perceptual illusions; doesn't make them any less human-level intelligent.

Re: Liquid Foundation Models: Our First Series of Generative AI Models

#68

Post by WhitneyLand »

alach11 wrote: post #33
zozbot234 wrote: post #30

> How many r's in "strawberry"? Think step by step! What a nice question! Well, let's just count: 1. S 2. T 3. R! So, we have our answer: there are 3 R's in "strawberry". Totally checks out!

This is one of the least interesting questions to ask LLMs. I wish it wasn't so popular.

I find it fascinating. To me it grounds expectations and motivates discussions about the jagged boundaries of LLM capabilities.

Re: Liquid Foundation Models: Our First Series of Generative AI Models

#69

Post by qeternity »

moralestapia wrote: post #35
alach11 wrote: post #33

Earlier quoted context omitted.

This is one of the least interesting questions to ask LLMs. I wish it wasn't so popular.

And yet ... all they have to do is answer it properly.

Training on the test set is all you need.

Re: Liquid Foundation Models: Our First Series of Generative AI Models

#70

Post by Jackson__ »

Jackson__ wrote: post #36

API only model, yet trying to compete with only open models in their benchmark image. Of course it'd be a complete embarrassment to see how hard it gets trounced by GPT4o and Claude 3.5, but that's par for the course if you don't want to release model weights, at least in my opinion.

I'd also like to point out that they omit Qwen2.5 14B from the benchmark because it doesn't fit their narrative(MMLU Pro score of 63.7[0]). This kind of listing-only-models-you-beat feels extremely shady to me.

[0] https://qwenlm.github.io/blog/qwen2.5/

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