[HN Gopher] What If We Recaption Billions of Web Images with LLa...
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What If We Recaption Billions of Web Images with LLaMA-3?
Author : Jimmc414
Score : 77 points
Date : 2024-06-13 03:44 UTC (19 hours ago)
(HTM) web link (arxiv.org)
(TXT) w3m dump (arxiv.org)
| Jimmc414 wrote:
| Code: https://github.com/UCSC-VLAA/Recap-DataComp-1B
| interloxia wrote:
| I'm a bit surprised by the examples. The original captions
| contain extra context rather than descriptions of the images.
|
| 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.
| petercooper wrote:
| Yes, the "Western Kingbird" is a good example of that. That's
| very useful context. I've not read the paper yet, but this
| could be trivially solved by just appending the extended
| description to the original one. You could ask the model if the
| original description seems accurate or not as well to weed out
| any duds (like the picture of a cake described as a 'twin
| room').
| skerit wrote:
| > The original captions contain extra context rather than
| descriptions of the images
|
| 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.
| nottorp wrote:
| Aren't the large public models censored? Who's going to caption
| the "offensive" images? And the porn?
| lwansbrough wrote:
| Well apparently you can just ablate those models!
| https://news.ycombinator.com/item?id=40665721
| GaggiX wrote:
| You cannot ablate the lack of knowledge, the vision models
| and projection layers used in this project usually lack any
| knowledge about NSFW, the model is simply unaware of what
| it's going on if you give them a NSFW image.
| nottorp wrote:
| Why assume offensive means NSFW?
|
| I'm saying these models are censored for the lowest common
| denominator.
| robertlagrant wrote:
| You mentioned pornography as well. And when people use
| "offensive" in an absolute sense, that often is
| synonymous with "what the HR department wouldn't want you
| looking at on a work computer", i.e. NSFW.
| nottorp wrote:
| I said offensive and porn. Separately. For a reason.
| robertlagrant wrote:
| Yes, you mentioned pornography. If you don't think that's
| safe for work, then why are you complaining about people
| talking about NSFW material?
| GaggiX wrote:
| I didn't assume anything, you mentioned porn.
| grondilu wrote:
| I'm quite baffled by the fact that LLMs can generate a dataset
| used to train other LLMs. One would think that such a feedback
| loop would produce utter nonsense but apparently not. This seems
| to work.
| cjg wrote:
| Humans have bootstrapped by training the next generation. Why
| not LLMs?
| spiderfarmer wrote:
| I think the perception is that humans can discover new
| information to question and improve what they learned, while
| LLM's cannot.
| immibis wrote:
| Human language drifts for the same reason LLM language
| would, but is continually reset to _a_ sensible state by
| interaction with the real world.
| DebtDeflation wrote:
| Yes, and there's even a name for it and associated area of
| research.
|
| https://en.wikipedia.org/wiki/Model_collapse
| bilater wrote:
| I feel the same way about synthetic data. Seems intuitively
| wrong that you can get new insights / unlock new abilities from
| generated data that you could not from the original data.
| throwthrowuknow wrote:
| If the correct labels in the original training set outweigh the
| incorrect ones then it is possible to reduce the number of
| errors by relabeling using the trained model. If you can also
| identify labels that are likely to be incorrect and then have
| humans focus on relabeling those you have a way to efficiently
| improve the data.
| michaelt wrote:
| _> first, we fine-tune a LLaMA-3-8B powered LLaVA-1.5 and then
| employ it to recaption ~1.3 billion images from the DataComp-1B
| dataset_
|
| 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.
| mjburgess wrote:
| Thew LLaMA model hasnt been trained on the low quality labels.
| Felomo wrote:
| If you train it on a belcurve, you will be able to fix the left
| side with it after.
| moconnor wrote:
| I'm not convinced the paper shows this "works". Table 3 clearly
| shows zero-shot image classification is worse with the
| recaptioned labels.
|
| 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.
| 42lux wrote:
| Yeah, I don't understand why they wouldn't use the captions
| that are already present as inputs as well.
| estebank wrote:
| That's what you'd do if you wanted to try and improve the
| captions, not if you wanted to evaluate the LLM's quality.
| vessenes wrote:
| Some good some bad here; it looks like you can in general get
| better descriptions out of LLama-3+Llava-1.5 than random crappy /
| short / possibly wrong human captions.
|
| 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.
| triyambakam wrote:
| > 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?
| vessenes wrote:
| Since they're using LLama with Llava, they're getting lots of
| Llama's baked-in understanding of the world. And I believe
| that Llama had multimodal training data as part of its
| pipeline, whether or not we get access to the heads that
| parse that data in the open weights.
|
| 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
| bastien2 wrote:
| Only if you have actual humans reviewing the output while being
| paid livable wages (i.e., you don't get to use digital sweatshops
| for this).
|
| 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.
| brianbreslin wrote:
| Not to start a political argument, but I am genuinely curious
| what you would consider a livable wage for a person in say
| Bangladesh doing this work?
| Jabrov wrote:
| About $1000 a month would be livable in Bangladesh
| kgwgk wrote:
| Definitely "livable" considering that the average household
| income per capita is an order of magnitude lower.
| pmontra wrote:
| 1000 Euro a month is something many young people don't get
| paid in Western Europe.
| greenhearth wrote:
| And what if we don't?
| galaxyLogic wrote:
| The caption is like the title of a painting. It does not so much
| tell what the image is an image of, but what the author/painter
| tells us their image represents. Represents, not depicts.
|
| 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.
| salamo wrote:
| I guess it depends on the purpose. As a query rewrite, these
| cover much more of the physical scene, but lack accuracy and
| specificity.
|
| 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.
| crote wrote:
| It's the difference between a caption and a description. Both
| are useful, just in different settings.
|
| 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.
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