I would be curious if this can do moderation with an arbitrary ruleset, or if it's just "that one moderation style" we already know from current big tech platforms.
The kind where malicious intent is okay if the words are nice.
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Or, rephrased: How big is the space in which you can tune this model without retraining.
Is it just "we hate sex"/"we don't hate sex" "We hate violence"/"we don't hate violence" or is it _truly_ as flexible as claimed?
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Maybe something like "Is this guy a corporate fraud that is going to waste my time with performative nonsense?"
That would be the true test for a moderation model and I would be immensely impressed if it could manage to pull that off.
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Edit:
Looking at the paper though.. probably not.
I suppose this is useful for B2B, which seems to be mistrals whole thing. Question is just if it is also useful for society to hand the SV prefab morals down like that. Kinda like cultural imperialism but with an ethical spin.
Maybe opinions on those base datasets could occasionally differ more than the model can be steered.
Also I do like Mistral's seemingly newer strategy of focusing on smaller, more fine-tuned models for various use-cases, presumably the result of their large MoE models not competing effectively with the frontier models.
It's not that their strategy is to train smaller models, it's the only choice they have. Training SOTA takes anywhere from 1.5b to 150b. We don't know the real cost of training for the chinese models, but mistral neither has the compute nor money to do that.
Mistral has the capability of training such models. Take a look at Poolside[1], they are claiming to pre-train their Laguna series of models on 4,096 NVIDIA H200 GPUs[2].
Mistral has approximately 13,800 NVIDIA GB300 GPUs, which are nearly 2x more efficient for training.
The problem with Mistral is that they do not seem to have aligned incentives to train big open-weight models, even if the teams would like to.
This model is way small for a proper assessment (imo). It should be very useful to study how big the real model must be for this purpose. Maybe merging it to a bigger one (adding it as expert style in moe) would be a solution!
Great job to Mistral team.
As for use cases, obviously we can't fully rely on non-deterministic capability for sensitive things but a small model which can do a good job acts as a first defense and then a human can review later.
I've had dreams of building something in the image sharing or social platform realm, but stopped short of planning because of obvious content moderation responsibilities. This seems to be a realistic, cost effective solution to that one piece of the puzzle.
I am not sure how reliable it is in the real world. Also, in terms of liability, I don’t know how effective it would be to satisfy various regulations compared to a human moderator team.
I hear ya, but one could set different operating thresholds: auto-approve low-risk posts, hold ambiguous posts for review, and automatically reject very high-confidence violations. So HITL for sure, but MUCH less H in the L.
Someone should use this to do the exact opposite of the intention: filter for “offensive” content, and boost it or collate it into a newsletter/email blast for people of culture.
You have to give it to Mistral they do at least know what the market near them says they want right now. The great problem is in a few years of this that market won’t be worth anything.
Edit to add, you could also add this to an AI workflow so as to produce content that walks right up to the line but doesn’t trigger it.
> The great problem is in a few years of this that market won’t be worth anything.
To be fair, we don’t know how much resources they put into this and how much of a distraction it was. If it was quick enough to train or fine tune and it brings them valuable experience for the next models, it could well be worth it in the long run even if there is no direct successor.
Also, it sounds like the kind of thing that sells. Any company with a customer support chat is a potential user of this model, any large company may be interested in getting a mistral installed set-up for handling without needing to send client info over the web. Installing those kinds of local systems seems to be what butters Mistral's bread at the moment.
this just sounds like new age edgey mc lordy lord. I get censorship is bad but ya'll might want to concern yourselves with a bid less fascism before you worry about the bad words police.
> they do at least know what the market near them says they want right now
It does seem to be a very European approach to AI that their flagship AI lab is just making models that do nothing other than monitor and moderate internet content.
I guess they know that the EU AI Act, Chat Control, etc are going to cause a lot of companies to need this kind of compliance.
Some of the best social media is heavily moderate. This includes HN and r/credible defense . With a Quiet transparent and cheap LLM I imagine a social media website where you can have good discussion about everything around the world it would be a game changer and on my to-do list.
The bulk of moderation work is things which are easy and obvious.
The correct way to moderate is automation with certainty falling back to humans with discretion.
The new frontier of moderation should be blocking illiterate comments, as in the commenter is replying as though they didn't read or read and didn't understand.
> It does seem to be a very European approach to AI that their flagship AI lab is just making models that do nothing other than monitor and moderate internet content.
Well, first Mistral is French more than European. This might be a difficult distinction to make from the US but their approach is quite different from e.g. typical German companies.
Then, this is just a small model they release on the side. If that’s your benchmark, they released somewhat recently Voxtral, Voxtral transcribe, their OCR model, and Leanstral. I don’t think you can get much insight on their culture from this kind of release.
Ugh. There's nothing inherently European about Chat Control. It's a dumb proposal, and it's European. Any free society has a bunch of dumb proposals.
Nor is there anything inherently European about the AI Act. But that one I wouldn't even call dumb. At times misguided and confused, perhaps, but some of its core principles are valuable.
By this logic the Chinese should have just given up and let the American AI companies have the market because they were so far behind. I'm sure Europe has the capability to distill other people's frontier models to catch up if they wish to do so.
Distilling is unsafe from export control perspective - Chinese models are poisoned by US frontier distillation and a case can be made that the US won’t like distilling what they may consider transitively theirs, which they will the moment you’re anywhere near competitive.
US judges have already rules that output of an LLM can't be copyrighted so not sure what would prevent Chinese companies to use said output for distillation purposes.
> already rules that output of an LLM can't be copyrighted
Mind sharing such cases? I'm not aware of any so far. There's the one with images, but that's commonly miss-understood, that case was ruled on a technicality (i.e. copyright needs to be attributed to a person, not a model)
By which other mechanism could American AI companies prevent this? Other companies don’t really care about EULAs and even if they needed to care it’s trivial to let third parties do it. Why would they? Almost nobody in the space cares about copyright and play fast and loose with laws and regulations.
What’s the mechanism that could today prevent other companies from using LLM outputs to train their models?
> Distilling is unsafe from export control perspective
That is not the direction American judges are taking. Right now, they are saying that LLM output cannot be copyrighted. And if looting copyrighted works for training is fair game, I really don’t see how one could argue that learning from other LLMs is not.
Their web chat UI seems to be called “Vibe.” I think? I’m not sure if that’s the name of the product or just what they decided to label it in the browser. I wonder if -strap is just what they call the actual models, which are meant to be run “under the hood” anyway, so not really part of the branding.
But I wish they could commit to the bit fully and call everything -stral. It’s quirky and self aware to give your products silly names.
The kind where malicious intent is okay if the words are nice.
___
Or, rephrased: How big is the space in which you can tune this model without retraining.
Is it just "we hate sex"/"we don't hate sex" "We hate violence"/"we don't hate violence" or is it _truly_ as flexible as claimed?
__
Maybe something like "Is this guy a corporate fraud that is going to waste my time with performative nonsense?"
That would be the true test for a moderation model and I would be immensely impressed if it could manage to pull that off.
___
Edit: Looking at the paper though.. probably not.
I suppose this is useful for B2B, which seems to be mistrals whole thing. Question is just if it is also useful for society to hand the SV prefab morals down like that. Kinda like cultural imperialism but with an ethical spin.
Maybe opinions on those base datasets could occasionally differ more than the model can be steered.
Also I do like Mistral's seemingly newer strategy of focusing on smaller, more fine-tuned models for various use-cases, presumably the result of their large MoE models not competing effectively with the frontier models.
The problem with Mistral is that they do not seem to have aligned incentives to train big open-weight models, even if the teams would like to.
[1]: https://poolside.ai/ [2]: https://poolside.ai/blog/introducing-laguna-s-2-1
As for use cases, obviously we can't fully rely on non-deterministic capability for sensitive things but a small model which can do a good job acts as a first defense and then a human can review later.
You have to give it to Mistral they do at least know what the market near them says they want right now. The great problem is in a few years of this that market won’t be worth anything.
Edit to add, you could also add this to an AI workflow so as to produce content that walks right up to the line but doesn’t trigger it.
I think that's the main service that xAI provide for X.
To be fair, we don’t know how much resources they put into this and how much of a distraction it was. If it was quick enough to train or fine tune and it brings them valuable experience for the next models, it could well be worth it in the long run even if there is no direct successor.
It does seem to be a very European approach to AI that their flagship AI lab is just making models that do nothing other than monitor and moderate internet content.
I guess they know that the EU AI Act, Chat Control, etc are going to cause a lot of companies to need this kind of compliance.
Heavily moderated by humans with discretion.
Not AI chat bots following a rules engine.
AI can do exactly that.
Pretty sure both of the above have extensive automation in their moderation.
The correct way to moderate is automation with certainty falling back to humans with discretion.
The new frontier of moderation should be blocking illiterate comments, as in the commenter is replying as though they didn't read or read and didn't understand.
Well, first Mistral is French more than European. This might be a difficult distinction to make from the US but their approach is quite different from e.g. typical German companies.
Then, this is just a small model they release on the side. If that’s your benchmark, they released somewhat recently Voxtral, Voxtral transcribe, their OCR model, and Leanstral. I don’t think you can get much insight on their culture from this kind of release.
Nor is there anything inherently European about the AI Act. But that one I wouldn't even call dumb. At times misguided and confused, perhaps, but some of its core principles are valuable.
They had kept up in the mid-range a few years ago. But this standing is sadly long gone.
If you need a fast Opensource'ed LLMs you can go for EU-hosted DeepSeek or Qwen.
Mind sharing such cases? I'm not aware of any so far. There's the one with images, but that's commonly miss-understood, that case was ruled on a technicality (i.e. copyright needs to be attributed to a person, not a model)
What’s the mechanism that could today prevent other companies from using LLM outputs to train their models?
That is not the direction American judges are taking. Right now, they are saying that LLM output cannot be copyrighted. And if looting copyrighted works for training is fair game, I really don’t see how one could argue that learning from other LLMs is not.
Mistral 7b is still one of the best free/open models you can run locally on a MacBook. So fast too.
"Shieldstral" is an awkward and bad name
But I wish they could commit to the bit fully and call everything -stral. It’s quirky and self aware to give your products silly names.
The stral the broke the camel's back?
Naming things is hard.