All the results that show improvement seem to be evaluations using LLMs, e.g. they are showing LLMs think the LLM-generated text is better, which is neither surprising nor expected to correlate with real downstream task performance - unless your final task is labelling for an LLM, e.g. retrieval I guess.

A couple of issues. First, the model completely botches the "Trike" brotherhood to "Tike", which also drops the meaning of the symbol. It's a tricycle. The seat isn't a sidecar, it's behind the driver's. Some of the numbers for the racecars also seem to be hallucinated.

It also doesn't seem accurate to describe the rewritten captions as having a "richer" vocabulary as the authors do. It's certainly more verbose and captures a lot of details about the setting, but lacks information density for the subject of the photo. You can't make up for low specificity with more high-probability words. Sometimes you need a long-tail word.

For example, the Western Kingbird caption. It's not just a grey and yellow bird. If the model doesn't know what a Western Kingbird is, you're not going to be able to draw a Western Kingbird no matter how verbose your description is.

A visually-impaired person reading the newspaper just wants to know that there is an image of, say, George Washington and John Adams. What do they look like? What are they wearing? What is the setting? Probably not important, it's just decoration for the article.

On the other hand, a visually-impaired person who's shopping for clothing might come across a t-shirt with a print and want to know what's on there. What colors are used? Is there any text? Are there logos? Stuff like that matters when you're choosing clothing because you don't want to end up looking like a clown.

The image generation models they train using the new caption system have better coherence and look a lot better / less weird. (I don’t know if they showed training detail differences between their comparisons, but let’s assume they’re not hiding the ball here).

The bad: Those captions don’t always perform better on all tests using existing infra - some of their tests perform worse against existing benchmarks.

Why they perform worse is an open question; the answer might be that the existing test data expects short / human answers, or it might be that the models are more verbose but less accurate (although that seems unlikely looking at the examples they show).

Also troubling to me is a toss-off note in Table 3 showing that the “concat” data, one in which they use original caption concatenated to the generated caption, has really bad CLIP outcomes. That … surprises me based on the rest of the results.

That said, their baseline comparison numbers in Table 1 are pretty compelling — this would definitely go into the ‘try it’ category based on the paper. But, it also goes into the ‘might fail in really surprising ways, so proceed carefully’ category for me.

EDIT: Another way to think of this model is to say “can we exfiltrate multi-model training data from open weights / closed training models?” And I think there the answer is yes.

Could you elaborate on this? Do you mean to say that this work reveals some of LLaMa's training data, or that this can be used to generate training data for new models?

So, I'm saying that Llama has some ability to describe / complete captions of photos, and very probably that ability comes from some multimodal training done at FB, and that using it in this way to train something new that beats benchmarks is a form of pulling out the value of that training data.

p.s. In the original comment, I meant to type 'multimodal' and didn't, or got autocorrected and didn't notice - apologies

Perhaps that doesn't matter, as the abstract suggests that's not the purpose.

On the other hand, training on captions they generate that are also incorrect is problematic. Perhaps they should have cherry-picked better examples without errors, such as details about the sugar and cream, reflections of the train in the water, or the location of the watermark and sign. I didn't look at the code, but perhaps each image feature can be written with a confidence score.

Another good example on how accessibility can even help people without disabilities. You can see this on ALT text on Mastodon, and I find it really useful.

Or consider a song, it's title does not tell where the sound was recorded, but what the composer tells us the song is about. Not where and when it was recorded.

It's a bit surprising this works as well as they report.

After all, you would think if you trained LLaVA on a dataset with low quality labels, you'd get a model that could only generate low quality labels, and re-labelling the dataset with the model would give you labels no better than the original low quality ones.

But considering Google has had over a decade to get autogenerated captioning right and they consistently fail at it, I doubt any slopshop is going to produce worthwhile output.

Of course it would make finding some images easier.

Problems with AI image relabeling might include: 1. AIs overuse some terms. If you search for a term the AI does not use, you find nothing. 2. AIs can be wrong. What if the AI misclassifies all 1967 Corvettes? 3. AIs are good at finding the big picture, but will it know that that is the only 1967 Corvette ever to leave the factory with those odd tail lights? (I'm making that up as an example.) 4. AIs might give overly general or overly specific labels. Is that a bit of technical clipart, a PCB layout, or part of an ESP32-S3-DevBoardC-1 PCB layout that has pins 3 of 4 switched in an obvious error?

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

I'm saying these models are censored for the lowest common denominator.