it utilizes the power of attention mechanisms to weigh the relevance of input data
By applying a technique called supervised fine-tuning across modalities, Meta was able to significantly boost CM3leon’s performance at image captioning, visual QA, and text-based editing. Despite being trained on just 3 billion text tokens, CM3leon matches or exceeds the results of other models trained on up to 100 billion tokens.
That’s a very fancy way to say they deliberately focussed it on a small set of information they chose, and that they also heavily configure the implementation. Isn’t it?
This sounds like hard-wiring in bias by accident, and I look forward to seeing comparisons with other models on that…
From the Meta paper:
“The ethical implications of image data sourcing in the domain of text-to-image generation have been a topic of considerable debate. In this study, we use only licensed images from Shutterstock.
As a result, we can avoid concerns related to images ownership and attribution, without sacrificing performance.”
Oh no. That… that was the only ethical concern they considered? They didn’t even do a language accuracy comparison? Data ethics got a whole 3 sentences?
For all the self-praise in the paper about state of the art accuracy on low input, and insisting I pronounce “CM3Leon” as Chameleon (no), it would have been interesting to see how well it describes people, not streetlights and pretzels. And how it evaluates text/images generated from outside the cultural context of its pre-defined dataset.
You are not logged in. However you can subscribe from another Fediverse account, for example Lemmy or Mastodon. To do this, paste the following into the search field of your instance: [email protected]
This is the official technology community of Lemmy.ml for all news related to creation and use of technology, and to facilitate civil, meaningful discussion around it.
Ask in DM before posting product reviews or ads. All such posts otherwise are subject to removal.
Rules:
1: All Lemmy rules apply
2: Do not post low effort posts
3: NEVER post naziped*gore stuff
4: Always post article URLs or their archived version URLs as sources, NOT screenshots. Help the blind users.
5: personal rants of Big Tech CEOs like Elon Musk are unwelcome (does not include posts about their companies affecting wide range of people)
6: no advertisement posts unless verified as legitimate and non-exploitative/non-consumerist
7: crypto related posts, unless essential, are disallowed
That’s a very fancy way to say they deliberately focussed it on a small set of information they chose, and that they also heavily configure the implementation. Isn’t it?
This sounds like hard-wiring in bias by accident, and I look forward to seeing comparisons with other models on that…
From the Meta paper:
Oh no. That… that was the only ethical concern they considered? They didn’t even do a language accuracy comparison? Data ethics got a whole 3 sentences?
For all the self-praise in the paper about state of the art accuracy on low input, and insisting I pronounce “CM3Leon” as Chameleon (no), it would have been interesting to see how well it describes people, not streetlights and pretzels. And how it evaluates text/images generated from outside the cultural context of its pre-defined dataset.