Can you save on LLM tokens using images instead of text?

48 pointsposted 3 months ago
by lpellis

20 Comments

bikeshaving

3 months ago

Does this mean we’ll finally get empirical proof for the aphorism “a picture is worth a thousand words”?

https://en.wikipedia.org/wiki/A_picture_is_worth_a_thousand_...

heltale

3 months ago

I suppose it’s only worth 256 words at a time right now. ;)

https://arxiv.org/abs/2010.11929

estebarb

3 months ago

The CALM paper https://shaochenze.github.io/blog/2025/CALM/ says it is possible to compress 4 tokens in a single embedding, so... image = 4×256=1024 words > 1000 words. QED

bikeshaving

3 months ago

2.4% relative error is not bad.

pastor_williams

3 months ago

Reminds me of Babbage making allowance for meter.

"""

    ... it is said that he [Babbage] sent the following letter to Alfred, Lord Tennyson about a couplet in "The Vision of Sin":

         Every minute dies a man,
         Every minute one is born

    I need hardly point out to you that this calculation would tend to keep the sum total of the world's population in a state of perpetual equipoise, whereas it is a well-known fact that the said sum total is constantly on the increase. I would therefore take the liberty of suggesting that in the next edition of your excellent poem the erroneous calculation to which I refer should be corrected as follows:

         Every minute dies a man,
         And one and a sixteenth is born

    I may add that the exact figures are 1.167, but something must, of course, be conceded to the laws of metre.
"""

    Charles Babbage and his Calculating Engines

cbhl

3 months ago

Shouldn't it be the other way around if the population is increasing? Every minute one is born = 1440 born/day, every minute and a sixteenth ~= 1335 dead/day for a net population increase of 105/day.

BrenBarn

3 months ago

It means that in every minute, one and a sixteenth of a man is born.

zahlman

3 months ago

Wouldn't "one and a sixth" be more accurate in both respects?

behnamoh

3 months ago

how do you decompress all those 4 words from one token?

estebarb

3 months ago

Not from one token, from one embedding. Text contains a low amount of information: it is possible to compress a few token embeddings into a single tiken embedding.

The how is variable. The calm paper seems to have used a MLP to compress from and ND input (N embeddings of size D) into a single D embedding and other for decompress them back

HarHarVeryFunny

3 months ago

The mechanism would be prediction (learnt during training), not decompression.

It's the same as LLMs being able to "decode" Base64, or work with sub-word tokens for that matter, it just learns to predict that:

<compressed representation> will be followed by (or preceded by) <decompressed representation>, or vice versa.

floodfx

3 months ago

Why are completion tokens more with image prompts yet the text output was about the same?

cma

3 months ago

Some multimodal models may have a hidden captioning step that may take completion tokens, others work on a fully native representation, and some do both I think.

Garlef

3 months ago

"Thinking" Mode

nunodonato

3 months ago

it doesn't say that anywhere.

user

3 months ago

[deleted]

ashed96

3 months ago

In my experience, LLMs tend to take noticeably longer to process images than text.

weird-eye-issue

3 months ago

It has to get the image data first, basically just IO time before processing it

ashed96

3 months ago

IIRC there's pre-processing (embedding/tokenization?) before feeding images to LLMs?

Hit this issue optimizing LLM request times. Ending up lowering image resolution. Lost some accuracy but could bear that.

psadri

3 months ago

I wonder if these stay in the prefix cache?