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points by lostmsu 1 day ago | 0 comments
Humans decidedly take much more data than LLMs. Like orders of magnitude. We happen to feed only distilled data to LLMs though. That's why they appear so efficient.
[–] rcxdude 1 day ago
If you are counting by bytes and including all sensory input, sure. In terms of amount of text, it's not close in the other direction. It's very difficult to make an apples to apples comparison here.
[–] lostmsu 1 day ago
You can obviously count by bytes. They are bytes for the model or for a person. Bytes to bytes is apples to apples.

You can't count by text, because text is not the only thing children learn on. So unlike the bytes comparison the text comparison is totally invalid.

[–] jibalt 15 hours ago
What they said, which is true, is that an LLM's inputs measured in bytes of language is much higher than for humans. Throwing in all the non-language bytes of sensory input received by humans is not valid and thus is not apples to apples. Children learning from watching a dog do zoomies around the room or butterflies in the garden is not learning language.

> We happen to feed only distilled data to LLMs though. That's why they appear so efficient.

That is wild handwaving that really explains nothing.

But then, most of the comments on this page are wild handwaving, pontificating like a blind man describing an elephant. Very little engages with or matches the quality of Norvig's arguments.

[–] lostmsu 14 hours ago
> Throwing in all the non-language bytes of sensory input received by humans is not valid and thus is not apples to apples. Children learning from watching a dog do zoomies around the room or butterflies in the garden is not learning language.

Have you ever had kids? Either you never had, you forgot, or you are intentionally deceiving us (and perhaps yourself) to support your point. If you never had kids, I can tell you you have very poor imagination about how they learn language.

[–] suddenlybananas 1 day ago
The BabyLM challenge has shown that multimodal models don't seem to learn in a more data-efficient way with respect to tokens. Adding an unlimited amount of images doesn't help you learn syntactic islands faster.
[–] lostmsu 1 day ago
Which entry are you referring to?
[–] suddenlybananas 1 day ago
Not a specific entry, but in four years, there's never been a clear advantage of multimodal models and the results have been extremely lackluckster, even when you allow unbounded images alongside a fixed quantity of text.
[–] lostmsu 22 hours ago
> when you allow unbounded images alongside a fixed quantity of text

Any specific entries?

[–] suddenlybananas 18 hours ago
You can look at the CFP for the most recent BabyLM https://arxiv.org/html/2602.20092v2 :

"This year, we have merged the Multimodal and Interaction tracks into the Strict and Strict-small tracks. This is for two primary reasons: Firstly, there were insufficient submissions to warrant standalone tracks. Secondly, none of the submissions in the Interaction track outperformed models submitted to the Strict track, and *none of the submissions from Multimodal outperformed our baselines*. We will still allow participants to use multimodal data or interactive training procedures if they choose, but such data or learning procedures must conform to the rules of the Strict or Strict-small tracks.

Emphasis mine.

[–] lostmsu 13 hours ago
> none of the submissions from Multimodal outperformed our baselines

I think that's a pretty weak point to your argument. They are saying that none of the contestants outperformed their baseline multimodal models and that there was very little multimodal submissions in the first place.